David Childers @DonskerClass Twitter profile

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David Childers

6 days

I'm restarting open Econometrics Office Hours this week, now from @BowdoinCollege , where I am visiting this year. Open to anyone anywhere needing econometrics help! Now Wednesdays 10-12 AM Eastern or by appointment.

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David Childers

@DonskerClass

6 months

Trying something new: starting next Wednesday, I will be hosting weekly Econometrics office hours free and open to anyone in the world with Econometrics questions. Sign up at

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David Childers

@DonskerClass

3 years

Updated notes for my ongoing Causal Econometrics class are now online, so far covering Causal Frameworks, Experiments, Adjustment, Semiparametrics, and IV. Please yell at me if you see anything you disagree with.

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David Childers

@DonskerClass

2 years

Some notes on stages of macroeconomic research ideas, inspired by attending a lot of HANK talks at this year's Society for Economic Dynamics meeting.

SED 2022 - Notes on contemporary macro | David Childers

I just got back from an excellent meeting of the Society for Economic Dynamics, a top conference for work in dynamic economics, principally but not exclusively in macroeconomics. As one of the first...

donskerclass.github.io

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David Childers

@DonskerClass

3 years

Starting tomorrow: teaching a new 1/2 semester PhD class: "Causal Econometrics" (CMU 47-873). Somebody told me this "causality" thing might be useful. Syllabus with prospective plans and readings: Comments welcome!

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David Childers

@DonskerClass

1 year

I'm making my Computational Methods for Economics class repo public this year. It's mostly classical numerical analysis and closely follows the Judd textbook and notes, but with a few updates and examples, so some may find it useful.

GitHub - donskerclass/ComputationalMethodsClass: Class Materials for 47-809 Computational Methods...

Class Materials for 47-809 Computational Methods for Economics at Carnegie Mellon - donskerclass/ComputationalMethodsClass

github.com

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David Childers

@DonskerClass

3 years

Found this linear algebra textbook at a local used book store. Seems to be a nonstandard presentation; is this what they teach in PolMeth?

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David Childers

@DonskerClass

2 years

Kernel methods provide a simple and robust solution to high dimensional (conditional) distribution estimation problems like those that show up in causal inference with continuous variables. This talk gave a compelling and comprehensible intro and how-to:

Zachary Lipton

@zacharylipton

2 years

kernels, causality, & conditioning, oh my! @ArthurGretton speaking to a rapt audience at a special @mldcmu seminar

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David Childers

@DonskerClass

5 years

Course notes for my class Econometrics II, including causal inference (potential outcomes and causal graphs), panel data, nonlinear estimation, and time series, with #rstats code.

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David Childers

@DonskerClass

3 years

New lecture on Time Series causal inference, focusing on identification, estimation, and causal interpretation of impulse response functions.

David Childers

@DonskerClass

3 years

Updated notes for my ongoing Causal Econometrics class are now online, so far covering Causal Frameworks, Experiments, Adjustment, Semiparametrics, and IV. Please yell at me if you see anything you disagree with.

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David Childers

@DonskerClass

2 years

This very long awaited work on the statistical properties of macroeconometric models is finally out. I believe the content of the critique deserves to be taken seriously by those of us working in the area.

Cosma Shalizi

@cshalizi

2 years

Your Favorite DSGE Sucks

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David Childers

@DonskerClass

4 years

The Laplacian is ubiquitous in financial and economic mathematics, where it represents evolution under continuous uncertainty. Geometric perspectives can explain its origin and relate its features to those of the underlying domain, and facilitate computation.

Keenan Crane

@keenanisalive

4 years

Just uploaded a video on the Laplacian: If you've heard of the Laplacian and always wondered what it was, or feel like you don't have great intuition for what it means, take a look—we explore how the Laplacian shows up in geometry, physics, & computation.

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David Childers

@DonskerClass

2 years

This project introduces new, easy-to-use tools for Bayesian estimation of nonlinear dynamic economic models. Try them out with DifferentiableStateSpaceModels.jl! 1/🧵

GitHub - HighDimensionalEconLab/DifferentiableStateSpaceModels.jl

Contribute to HighDimensionalEconLab/DifferentiableStateSpaceModels.jl development by creating an account on GitHub.

github.com

NBER

@nberpubs

2 years

Hamiltonian Monte Carlo is a great approach to estimating dynamic models in economics: open-source libraries to do so, from @DonskerClass , Jesús Fernández-Villaverde, Jesse Perla, @ChrisRackauckas , and Peifan Wu

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David Childers

@DonskerClass

1 year

Added notebooks on numerics for optimal control, from Pontryagin to HJB, to my Computational Econ class notes. Julia: Python:

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David Childers

@DonskerClass

4 years

Where frequentism really excels is at easy semiparametrics: the intersection of "I don't know" and "I don't care." You can focus on the things you need to get right like treatment effects and be robust to nuisances like error forms or serial correlation 🧵

Jeffrey Wooldridge

@jmwooldridge

4 years

But those estimators can be studied from a frequentist perspective, and no strong assumptions are needed. My hesitation with Bayesian methods -- when they differ from classical ones -- is that they are not "robust" in the econometrics sense.

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David Childers

@DonskerClass

3 years

I closed out my Causal Econometrics class with a grab bag of lectures on Outcome Heterogeneity Decision-making and Optimal Policy and External Validity and Sample Selection

David Childers

@DonskerClass

3 years

Updated notes for my ongoing Causal Econometrics class are now online, so far covering Causal Frameworks, Experiments, Adjustment, Semiparametrics, and IV. Please yell at me if you see anything you disagree with.

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David Childers

@DonskerClass

7 years

Take some time out this weekend to learn about statistical learning and uniform sequential prediction

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David Childers

@DonskerClass

5 years

Notes from my class, Forecasting for Economics and Business, covering statistical, Bayesian, online, and machine learning methods for forecasting and time series, with applications and code in #rstats

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David Childers

@DonskerClass

2 years

Not to beat a dead horse, but the same reason nobody else does, which is that it violates the laws of physics.

Some issues with Bayesian epistemology | David Childers

In this post, I’d like to lay out a few questions and concerns I have about Bayesianism and Bayesian decision theory as a normative theory of inductive inference. As a positive theory, of what people...

donskerclass.github.io

Liam Bright

@lastpositivist

2 years

Why don't journalists just use Bayesiansm?

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David Childers

@DonskerClass

6 years

For @jim_savage_ , a reading list of good recent work on the empirical side in heterogeneous agent macro

An Empirical Heterogeneous Agents Models Reading List | David Childers

Inspired by a request by Jim Savage asking for examples of recent work using heterogeneous agent models, I’ve put together a far from comprehensive list of papers demonstrating the range of work...

donskerclass.github.io

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David Childers

@DonskerClass

3 years

@instrumenthull This can also give big computational advantages for 2 step estimates: you can run the (expensive) step 1 once and plug in, then use reweighted draws of the same influence function, instead of doing both steps in every bootstrap resample. See Section 3 of

Program Evaluation and Causal Inference with High-Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in...

arxiv.org

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David Childers

@DonskerClass

1 year

Had a lovely visit to @Brown_Economics , where @instrumenthull was the first person ever to ask about the profile pic. It is of course a realization of a Karhunen-Loeve decomposition of a Brownian bridge, the limit distribution implied by Donsker’s theorem.

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David Childers

@DonskerClass

8 months

Some brief and introductory notes for an informal talk to a Machine Learning audience on how economists and ML researchers model preferences.

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David Childers

@DonskerClass

6 months

Week 1 of Econometrics Office Hours went well. Thanks to everyone who signed up or asked questions! Sessions continue next week, same time. Sign up at or by form at

Econometric Consulting Office Hours

Request form to get the link for Econometrics consulting office hours or to sign up for another time, and to provide any info you want about your question.

docs.google.com

David Childers

@DonskerClass

6 months

Trying something new: starting next Wednesday, I will be hosting weekly Econometrics office hours free and open to anyone in the world with Econometrics questions. Sign up at

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David Childers

@DonskerClass

3 years

Amazing resource! Explains how to derive a nonparametric influence function estimator (eg for DoubleML, TMLE, etc) using basic calculus without assuming you know the jargon or skipping over the key finicky bits of algebra.

Ollie Hines

@hines8

3 years

Modern statistical methods often use estimators based on efficient influence functions. See our latest tutorial paper on the subject with Oliver Dukes, @karlado @SVansteelandt

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David Childers

@DonskerClass

8 months

Reviving an old end of year tradition: a post on some papers I read and liked in 2023

Papers I Liked 2023 | David Childers

I felt like I barely did any serious reading this year, and maybe that’s even true, but my read folder contains 168 papers for 2023, so even subtracting the ones that are in there by mistake, that’s...

donskerclass.github.io

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David Childers

@DonskerClass

3 years

2021 updated notes from my class Forecasting for Economics and Business, covering statistical, Bayesian, online, and machine learning forecasting methods with code in #RStats

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David Childers

@DonskerClass

2 years

Lots to say about this (fascinating!) work, but I'll start with: it's one of the only critiques of "how did economics get like that" to seriously engage with what most economists actually did in policy (at least up to ~1985), and not just cover a few unrepresentative celebrities.

Beth Popp Berman (epopppp@bsky)

@epopppp

2 years

After 10 years, 4 drafts, 3 houses, 2 jobs, a pandemic and a life-threatening illness, it’s publication day! A book thread.

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David Childers

@DonskerClass

3 years

Per end of year tradition, a list of some papers I liked in 2021

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David Childers

@DonskerClass

4 years

A year end list of some of the favorite papers I read in 2020 featuring Bayes, reinforcement learning, and a bit of macroeconomics.

Top Papers 2020 | David Childers

The following is a look back at my reading for 2020, identifying a totally subjective set of the top 10 papers I read this year. My reading patterns, as usual, have not been so systematic, so if your...

donskerclass.github.io

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David Childers

@DonskerClass

3 years

This was a fun project combining the Generalized Method of Moments with online learning to choose sequentially from multiple data sources to estimate a parameter. @shantanug7 and @zacharylipton taught me a lot about machine learning in the process.

ACMI Lab (CMU)

@acmi_lab

3 years

Congrats @shantanug7 (& co-advisor @DonskerClass ) on #neurips2021 spotlight! While data fusion problems are old hat (esp in econ/causal), the available data are always given. Here, we iteratively decide what source to query to converge on our parameter.

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David Childers

@DonskerClass

7 years

Following @paulgp and @Noahpinion , here's a list of the top 10 (mostly econometrics) papers I enjoyed reading in 2017

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David Childers

@DonskerClass

6 years

Preliminary draft on a bit of theory and experiments for heterogeneous agent algorithms

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David Childers

@DonskerClass

5 years

Important new paper (by Auclert, Bardóczy, Rognlie & Straub) on solution of linearized heterogeneous agent methods based on the sequence space (Wold or MA(∞)) representation (1)

GitHub - shade-econ/sequence-jacobian: A unified framework to solve and analyze heterogeneous-agent...

A unified framework to solve and analyze heterogeneous-agent macro models. - shade-econ/sequence-jacobian

github.com

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David Childers

@DonskerClass

4 years

Some quick notes on choosing a posterior sampling algorithm in @TuringLang

Posterior Samplers for Turing.jl | David Childers

Prompted by a question on the slack for Turing.jl about when to use which Bayesian sampling algorithms for which kinds of problems, I compiled a quick off-the-cuff summary of my opinions on specific...

donskerclass.github.io

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David Childers

@DonskerClass

4 years

@nickchk Aside from recent DiD papers (on their way to wide use): -Formal mediation analysis: claims about mechanisms are causal and a well identified main effect is not enough. -For data exploration, quantile or distributional random forests. Conditional distributions tell you a lot!

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David Childers

@DonskerClass

5 years

I'm at the Poster Session in Econometrics today. Ballroom E 8:00-10:00am. Drop by!

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David Childers

@DonskerClass

4 years

@lewbel @PhilHaile @yudapearl @steventberry @VC31415 I have a paper defining DSGE structure in terms of DAGs: see Cdn 3. Auclert et al do likewise, and have pictures: eg in both cases, per "GE", this a computational graph of equilibrium conditions: the full equilibrium is not acyclic.

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David Childers

@DonskerClass

6 years

Survey for practitioners on the current state of research on inference in linear instrumental variables models with weak instruments.

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David Childers

@DonskerClass

2 years

Economists should be aware of recent remarkable work by Alon et al showing equivalence between Private PAC learnability and finite Littlestone dimension. In words, this says precisely what functions will be consistently estimable from private census data.

An Equivalence Between Private Classification and Online Prediction

Shay Moran (Technion)https://simons.berkeley.edu/talks/equivalence-between-private-classification-and-online-predictionLearning and Testing in High Dimensions

www.youtube.com

The Shaw Prize

@ShawPrize

2 years

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David Childers

@DonskerClass

2 years

After jazz and beignets, the highlight of my #neurips22 was learning from Masa Uehara about the deep connections between causal inference and policy evaluation in reinforcement learning. He now has a "Rosetta Stone" survey paper about it, w/ Shi & Kallus

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David Childers

@DonskerClass

4 years

Did the thing. Let's go Pennsylvania!

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David Childers

@DonskerClass

4 years

@zacharylipton Interdisciplinary reading group? Cosma teaches learning theory, @KevinZollman teaches epistemology, I teach... one handwavy lecture rushing through Hume, Bayes, Wald, and Wolpert before spending the rest of a semester on basic R. Others might be interested too.

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David Childers

@DonskerClass

6 years

Some remarkable course notes by Costas Arkolakis on spatial economics

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David Childers

@DonskerClass

2 years

My MCMC is running beautifully today, after some tweaking, but I love the idea of some journalist just deciding that some perspective on an issue should be ignored because otherwise the log Sobolev constant for their sampler would to too large to make the deadline.

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David Childers

@DonskerClass

4 years

Question about common pattern of persistent forecast revisions below (ht @Claudia_Sahm ): how can its' ubiquity be rationalized, given that Bayes forecasts should be martingales (revisions mean 0)? 3 conjectures (please chime in with others):

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David Childers

@DonskerClass

3 years

Frequentist guarantees form a "contract" with the data analyst, providing that if you do certain things to your data, your analysis will have certain properties. But real data analysis inevitably involves other steps, which can bork traditional guarantees.

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David Childers

@DonskerClass

7 years

To coworker, on my switch to #JuliaLang : "It's like Matlab but with [ ] instead of ( ) and doesn't die when you try to write a for loop"

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David Childers

@DonskerClass

3 years

Retweeting to promote publicity for demonstrations of methods working poorly outside of their ideal domain of applicability and showcase examples in papers. Performance issues are often known to practitioners (and implicit in papers' guarantees), but passed on mainly as folklore.

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David Childers

@DonskerClass

2 years

Since my grad students told me they read my tweets, I'd like to mention that the Simons Institute videos on Learning & Games are a great use of time for anyone interested in computation and estimation in econ, especially the boot camp to get up to speed.

Learning and Games

By bringing together researchers from machine learning, economics, operations research, theoretical computer science, and social computing, this program aims to advance the connections between...

simons.berkeley.edu

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David Childers

@DonskerClass

4 years

@VC31415 I use ggdag along with dagitty for DAG visualization in R. In Python, see dowhy as well as and (in beta, ping @eliasbareinboim for previews) for more estimation and identification tools.

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David Childers

@DonskerClass

3 years

Added lecture notes on Difference in Differences and Regression Discontinuity

David Childers

@DonskerClass

3 years

Updated notes for my ongoing Causal Econometrics class are now online, so far covering Causal Frameworks, Experiments, Adjustment, Semiparametrics, and IV. Please yell at me if you see anything you disagree with.

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David Childers

@DonskerClass

5 months

Happening again this afternoon, 4:00-6:00 Eastern! Sign up and link at

David Childers

@DonskerClass

6 months

Trying something new: starting next Wednesday, I will be hosting weekly Econometrics office hours free and open to anyone in the world with Econometrics questions. Sign up at

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David Childers

@DonskerClass

2 years

PSA that due to long-ago choice of username, I am morally obligated to engage with any tweet regarding Donsker's theorem. Please use this power wisely.

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David Childers

@DonskerClass

3 years

Liked this book, had some thoughts: Tl;dr: Definitely read it if you want to have a worthwhile theory section in your empirical paper or vice versa.

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David Childers

@DonskerClass

6 years

Reading Pearl's latest book and seeing economists debate variable selection have really emphasized how badly current instruction that ignores the backdoor criterion has prepared our profession to think clearly about causal inference from observational data.

scott cunningham

@causalinf

6 years

As I’ve said before, one of the reasons to at least familiarize yourself with Pearls work is for the idea of a “collider”, as well as for reminding ourselves that theory dictates the selection of covariates and to ignore that opens us up to bias.

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David Childers

@DonskerClass

4 years

@Undercoverhist Some of my old thoughts on mechanism design as an extension of the planning problem, following Shalizi on the computational aspects.

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David Childers

@DonskerClass

5 years

Learning optimal macro policies from micro experiments (using online convex optimization on a mean field model) by Wager & Xu.

Experimenting in Equilibrium

Classical approaches to experimental design assume that intervening on one unit does not affect other units. There are many important settings, however, where this non-interference assumption does...

arxiv.org

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David Childers

@DonskerClass

3 years

Nice review of advances in analysis of matched firm-worker "bipartite network" data, including issues with AKM-style two-way fixed effects. To start a fire: "limited mobility bias" is distinct from heterogeneity bias in DiD. Thoughts on how they interact?

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David Childers

@DonskerClass

3 years

Bravo! Isaiah has provided simple and practical methods for hard problems like interpretation, communication, and publication bias of estimates, using asymptotic theory to distill down to simple decision-theory problems using localization.

AEA Information

@AEAInformation

3 years

Congratulations to Isaiah Andrews of @Harvard , winner of the 2021 John Bates Clark Medal!

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David Childers

@DonskerClass

4 years

Some idle musings on why I, as a user of Bayesian statistical methods, don't find Bayesianism normatively compelling as a theory of inductive inference

Some issues with Bayesian epistemology | David Childers

In this post, I’d like to lay out a few questions and concerns I have about Bayesianism and Bayesian decision theory as a normative theory of inductive inference. As a positive theory, of what people...

donskerclass.github.io

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David Childers

@DonskerClass

7 years

Anh Nguyen uses variation in government health insurance plan offerings to estimate a nonlinear multilevel model of adverse selection & moral hazard using @mcmc_stan . She will be joining CMU econ this year.

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David Childers

@DonskerClass

2 years

To the philosopher of Quantum Mechanics who left @bayesianboy 's with my shoes last night, I see how Everettianism raises problems for determinate identity, but I don't understand decoherence well enough to draw macroscopic implications. Also, contact me, I have your shoes.

Mel Andrews

@bayesianboy

2 years

Had a small housewarming party upon moving to Pittsburgh and the shoes of a CMU faculty member were stolen under mysterious circumstances.

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David Childers

@DonskerClass

1 year

@causalinf There's a long history of IV Phillips curve estimates, albeit beset by hetrogeneity, specification search, exclusion and validity issues, weak IV, etc, leading to a wide range of estimates. FWIW the IV you describe is close to Jordà Schularick & Taylor's "trilemma instrument."

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David Childers

@DonskerClass

3 years

The following may be helpful for when you find yourself, as one does, estimating a VAR after March 2020.

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David Childers

@DonskerClass

6 years

Important new paper by Kocherlakota provides new foundations for causal inference in macroeconomics, linking game theoretic equilibrium and modern causal inference assumptions

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David Childers

@DonskerClass

7 months

Translating for classical time series folks, their STU is a projection onto a 1-sided filter defined by Hankel eigenfunctions after prewhitening, and they give approximation results in a Local-to-Unity model. Reminds me of which is 2-sided.

Elad Hazan

@HazanPrinceton

7 months

most exciting paper *ever* from our @GoogleAI lab at @Princeton : @naman33k @danielsuo @XinyiChen2 *** Convolutional filters predetermined by the theory, no learning needed! ***

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David Childers

@DonskerClass

4 years

or you need real Bayesian semiparametrics with ∞-dimensional priors expressing uncertainty about everything you left out then marginalizing (see for tutorial)

Arman Oganisian

@StableMarkets

4 years

@jmwooldridge @lauretig @mcmc_stan @pymc_devs Yep can handle complicated forms for error without specification via some nonparametrics. Here is a recent example. Dirichlet Process mixture on the left. spline on right. All I did was specify dep. & ind. vars.

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David Childers

@DonskerClass

3 years

@twiecki Mostly tradition, though my experience coding BVARs in Stan is that unless BLAS code has improved a lot Gibbs methods are in fact orders of magnitude faster and more reliable. And more complex models can't run at all without backend hacks (I have a paper on this forthcoming).

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David Childers

@DonskerClass

2 years

To get some thoughts on paper: a longer review of "Thinking Like an Economist"

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David Childers

@DonskerClass

5 years

A useful survey of the burgeoning field of information design, which examines the outcomes that can be achieved by providing or withholding information from rational strategic agents, as via recommender systems, certifications, or aggregators.

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David Childers

@DonskerClass

4 years

In a recent interview, @ben_moll described the Kolmogorov Forward Equation as the most important equation in macroeconomics. His notes are a great introduction to these applications

lectures - Benjamin Moll

Benjamin Moll Lecture notes and other presentation slides 2024 Heterogeneous Agent Macroeconomics: Eight Lessons and a Challenge Economic Journal Lecture (Royal Economic Society), Video Recording...

benjaminmoll.com

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David Childers

@DonskerClass

4 years

@nickchk R packages for random forests for distributions: (and also grf on CRAN) Seeing these always tells me a lot about my data I wouldn't know otherwise.

GitHub - lee-group-cmu/RFCDE: Random Forests for Conditional Density Estimation

Random Forests for Conditional Density Estimation. Contribute to lee-group-cmu/RFCDE development by creating an account on GitHub.

github.com

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David Childers

@DonskerClass

3 years

@sam_power_825 Understandable that you would be concerned about such things, since so few credit you for your Method for computing eigenvectors.

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David Childers

@DonskerClass

5 years

Intriguing new approach used for Hamilton-Jacobi-Bellman equations in up to 10,000 dimensions. (Uses tricks based on Feynman-Kac representations and neural networks.)

Deep splitting method for parabolic PDEs

In this paper we introduce a numerical method for nonlinear parabolic PDEs that combines operator splitting with deep learning. It divides the PDE approximation problem into a sequence of separate...

arxiv.org

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David Childers

@DonskerClass

2 years

Love to debate the finer points of Bayesian epistemology when the actual purpose of Bayes is to see the words "LAPACKException" print on my screen 100 times a second for 4 days (and counting) on a run I am almost certain will fail.

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David Childers

@DonskerClass

1 year

@sp_monte_carlo In my computation class, I introduce FBSDEs as the path space dual to HJB equations as a stochastic analogue to dynamic programming vs optimal control, with similar pros & cons. was a helpful survey of applications; @momin_rayhan collects more.

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David Childers

@DonskerClass

3 years

@omaclaren Don't make me tap the sign...

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David Childers

@DonskerClass

3 years

@PossebomVitor @causalinf @nickchk @MatheusFacure I teach a class centered on the Athanasopoulos and Hyndman Forecasting book that everyone else is (rightly) recommending, but I add on notes with added topics and extensive additional R code examples and exercises:

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David Childers

@DonskerClass

4 years

A foundational work of data visualization, inspirational for any data or social scientist in your life. P.S.: Data CSVs and example code at thanks to @ajstarks

Recreating W.E.B Du Bois’s Data Portraits

In order to understand the background and motivation of these charts, I decided to recreate them with as much precision as possible

medium.com

b-boy bouiebaisse

@jbouie

4 years

my wife got me this for christmas and i’m thrilled

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David Childers

@DonskerClass

4 years

The women of Harvard Law Class of '59, including a profile of my grandmother, Marilyn Rose, and her contributions to civil rights law.

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David Childers

@DonskerClass

5 years

MCMC does not seem to play well with non-identified parameters with unbounded support and nearly flat priors, huh. (This tweet brought to you by the number 10^153.)

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David Childers

@DonskerClass

2 years

@jmwooldridge You are correct: see @isabelfulcher et al for its characterization as one type of indirect effect. Under frontdoor assumptions, the indirect effect is the total effect, but their estimator (code at ) is valid without that.

GitHub - isabelfulcher/frontdoorpiie: Estimation and inference for the Population Intervention...

Estimation and inference for the Population Intervention Indirect Effect (PIIE) - isabelfulcher/frontdoorpiie

github.com

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David Childers

@DonskerClass

7 years

Chen's contributions: use only the theory you believe to impose structure, use nonparametrics to let data handle the rest. Well deserved!

Manuel Arellano

@manolo_arellano

7 years

Gregory Chow and Xiaohong Chen awarded the China Economics Prize for their outstanding contributions to econometrics

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David Childers

@DonskerClass

4 years

@VC31415 Unfaithful distributions are measure 0 but dense, such that the Markov structure is not uniformly estimable: see S4 of Whether this can be resolved by stronger uniform versions of faithfulness is an interesting question I'm not certain about.

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David Childers

@DonskerClass

1 year

@sp_monte_carlo Distinguishing the objective and the probability model has been a mainstay of econometrics since White 1980. Aronow & Miller's Agnostic Statistics is an intro textbook with that perspective. Berk et al 2019 is a nice expository piece for statisticians.

Assumption Lean Regression

It is well known that with observational data, models used in conventional regression analyses are commonly misspecified. Yet in practice, one tends to proceed with interpretations and inferences t...

www.tandfonline.com

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David Childers

@DonskerClass

4 years

In macroeconomics, by the Kolmogorov Forward Equation, it describes how a population evolves under persistent forces and impulses, and dually, generates the expectations that a rational agent would use to make plans under that evolution via the HJB equation.

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David Childers

@DonskerClass

2 years

@Andrew___Baker @rlmcelreath Assuming you're not interested in macro (in which case Schorfheide's books & chapters are the place), Peter Rossi's work is clear and builds up to real applicable methods. For modern PPL-focused approaches, I don't know of an econ-specific book, though @abiylfoyp was trying.

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David Childers

@DonskerClass

11 months

@xuanalogue This is the point of SWIGs. Single World Intervention Graphs represent intervened nodes by splitting. The Hernán and Robins textbook uses them extensively.

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David Childers

@DonskerClass

2 years

@economeager @btshapir @c_r_walt A friend of mine who was a survey enumerator for a well known development economist before grad school told me that on particularly hot days they would just make the numbers up and go smoke weed in the shade. Real world motivation for the Huber contamination model!

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David Childers

@DonskerClass

2 years

“I would like to see Yusuke continue a very promising career as a scholar,” Dr. Angrist said. “So my main concern in a case like his is that he’s being distracted by other things, and that’s kind of a shame.” Can someone who speaks Japanese explain what's going on here?

Motoko Rich

@motokorich

2 years

Yusuke Narita, a Yale economist, has pushed Japan's hottest button his suggestions that the elderly commit "mass suicide". He says his comments were "taken out of context;" his critics accuse him of hate speech

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David Childers

@DonskerClass

8 years

@johnmyleswhite has some modern tools. Ch20 of Efron & Hastie's latest book covers classical methods

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David Childers

@DonskerClass

3 years

@sp_monte_carlo @omaclaren Always applicable

Zach Weinersmith

@ZachWeiner

4 years

Bayesian (click for full comic) #smbc #hiveworks

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David Childers

@DonskerClass

1 year

In NYC today through Sunday, in part for Machine Learning for Healthcare 2023. Send bagel place recommendations. 🥯

Machine Learning for Healthcare

www.mlforhc.org

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David Childers

@DonskerClass

6 years

@adamkapor This blog post is a start. The daggity users guide @_MiguelHernan 's textbook ( @causalinf 's textbook is on my to read list) Pearl's survey Hal White's papers introduced me to the topic (1/2)

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David Childers

@DonskerClass

5 months

@ben_golub Backtracking references, the answer seems to be yes there are genericity results for *local* Nash eq'a of 2 player games. Screenshot in , found in survey , referencing result in

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David Childers

@DonskerClass

6 years

Welcome to twitter my brilliant colleague @edwardhkennedy ! If you are interested in causal inference, follow him, and use his #rstats package for doubly robust nonparametric treatment effect estimation using machine learning.

GitHub - ehkennedy/npcausal

Contribute to ehkennedy/npcausal development by creating an account on GitHub.

github.com

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David Childers

@DonskerClass

6 years

@morganmpage Singapore has this! Every tree, mapped, searchable by species or location. Would love to see this in more cities.

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David Childers

@DonskerClass

2 years

@jamesbrandecon There's a nice paper by Serena Ng on the practice of sketching methods for regressions when X is too big to fit in memory that may have some useful advice

Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data

www.nber.org

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David Childers

@DonskerClass

3 years

@Andrew___Baker @jiafengchen42 @a_strezh @matt_blackwell @asheshrambachan @shephard_neil @IavorBojinov Weak overlap increases exponentially in T (same reason particle filters are trash), so IPW can be highly variable. Consider stable estimands like incremental interventions . Ed, Larry, & Siva mentioned yesterday work in progress on debiased trimming methods

Incremental causal effects: an introduction and review

In this chapter, we review the class of causal effects based on incremental propensity scores interventions proposed by Kennedy [2019]. The aim of incremental propensity score interventions is to...

arxiv.org

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David Childers

@DonskerClass

2 years

A notebook with a bonus example of what you can do with DifferentiableStateSpaceModels.jl: add heavy tails and stochastic volatility to your macro model. Probabilistic programming makes it easy to add model features without having to redesign your code.

David Childers

@DonskerClass

2 years

This project introduces new, easy-to-use tools for Bayesian estimation of nonlinear dynamic economic models. Try them out with DifferentiableStateSpaceModels.jl! 1/🧵

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