Me

Me trying to keep up with the DiD literature.

Just posted updated version of our DID textbook! We now have drafts of all chapters, including the one on general designs! Now you can tell your friends still on X that they are DID-outdated :-) Happy easter for those of you that celebrate it. papers.ssrn.com/sol3/papers....

Kirill Borusyak, Mauricio Caceres Bravo, Peter Hull: Estimating Demand with Recentered Instruments https://arxiv.org/abs/2504.04056 https://arxiv.org/pdf/2504.04056 https://arxiv.org/html/2504.04056

link 📈🤖
Distributional Instrumental Variable Method (Holovchak, Saengkyongam, Meinshausen et al) The instrumental variable (IV) approach is commonly used to infer causal effects in the presence of unmeasured confounding. Conventional IV models commonly make the additive noise assumption, which is

GitHub - clibassi/python-packages-for-applied-economists: A curated collection of Python packages for applied economists, organized by functionality to support econometric analysis, data management, v... A curated collection of Python packages for applied economists, organized by functionality to support econometric analysis, data management, visualization, and specialized tasks. - clibassi/python-...

Okay, I made an updated version of the guide "Python Packages for Applied Economists" to reorganize a bit, incorporate suggestions, and put it on Github like a grownup: github.com/clibassi/pyt...

Comments welcome!

2

Susan Athey and/or Daron Acemoglu

A Flexible, Heterogeneous Treatment Effects Difference-in-Differences Estimator for Repeated Cross-Sections Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...

I cannot wait to dive into this new simpler approach for Diff in Diff even though I am no longer programming my own Stata code. :-) #EconSky @edwardnorton.bsky.social @edwardnorton.bsky.social @jmwooldridge.bsky.social

www.nber.org/papers/w33026

#EconSky This is a brand new book by Chernozhukov et al on state of the art causal machine learning methods.
causalml-book.org

Hi #EconSky. Greatly appreciate any suggestion on open data about inflation expectations. Thanks.

Stacked Difference-in-Differences Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...

#EconSky: This is a new WP on stacked DiD
www.nber.org/papers/w32054

I don't think do. This takes another approach for the estimation of counterfactuals

Things are still going on so not really sure, thanks

Model Averaging and Double Machine Learning This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. We...

Hi #EconSky. This is a new cool WP by Ahrens et al on the benefit of combining DDML with stacking for causal inference.
arxiv.org/abs/2401.01645

Causal Models for Longitudinal and Panel Data: A Survey This survey discusses the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data,...

#EconSky: A hot new survey WP have been dropped by the masters Arkhangelsky & Imbens on causal models for longitudinal and panel data. A must read if you want to cover everything from DiD & TWFE estimators to nonlinear models, synthetic controls, & design-based inference.
arxiv.org/abs/2311.15458

Machine Learning for Staggered Difference-in-Differences and... We combine two recently proposed nonparametric difference-in-differences methods, extending them to enable the examination of treatment effect heterogeneity in the staggered adoption setting using...

#EconSky: This is a new cool WP on ML-DiD with staggered adoption.
arxiv.org/abs/2310.11962

Designing Difference in Difference Studies With Staggered Treatment Adoption: Key Concepts and Pract... Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...

#EconSky: a very practical WP on DiD designs with staggered adoption.
www.nber.org/papers/w31842

Covariate Balancing and the Equivalence of Weighting and Doubly... We show that when the propensity score is estimated using a suitable covariate balancing procedure, the commonly used inverse probability weighting (IPW) estimator, augmented inverse probability...

#EconSky If you're still diving in the ocean of propensity score, this is a very practical new WP that establishes very useful equivalence results when using the inverse probability tilting and the covariate balance propensity score methods.
arxiv.org/abs/2310.18563

Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Cont... Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...

#EconSky: This is a very cool brand new WP by Spiess, Imbens and Venugopal on exploiting double and single-descent phenomenon in ML to deal with highly over parameterized models in causal inference, including synthetic control with many control units.
www.nber.org/papers/w31802

Trimmed Mean Group Estimation of Average Treatment Effects in... Under correlated heterogeneity, the commonly used two-way fixed effects estimator is biased and can lead to misleading inference. This paper proposes a new trimmed mean group (TMG) estimator which...

#EconSky: FE-TE estimator is biased under correlated heterogeneity (CH). Pesaran & Yang propose in brand new WP a test of CH which works well even if the time dimension is VERY short. To avoid bias they recommend using a new trimmed mean group estimator.
arxiv.org/abs/2310.11680

Sensitivity analysis for principal ignorability violation in... An important strategy for identifying principal causal effects, which are often used in settings with noncompliance, is to invoke the principal ignorability (PI) assumption. As PI is untestable,...

#EconSky: principal ignorability (PI) assumption is usually invoked to estimate causal effects of compliers vs. non compliers. This is a cool WP by the team of Nguyen et al., tailoring new sensitive techniques for several PI based methods.
arxiv.org/abs/2303.05032

#EconSky: if you don't have a control group don't worry. This is cool WP allowing forecasting of counterfactuals by ML in your causal panel analysis. A dedicated R package is accompanying this methodology.
papers.ssrn.com/sol3/papers....

🔮 The model predicts two distinct optimal relationships between sustainable development and natural resource depletion. This relationship depends on the existence of a 6 billion tons threshold for natural resource depletion (9/n).

🌱 Optimal sustainable development, as revealed by the model, exhibits a quasi-inverse U shape which raises important questions about the long-term sustainability of sustainable development itself (8/n).

⏭️ By simulating the model for the time horizon 2020-2100, a non-linear pattern of natural resource depletion was found. Interestingly, the projections indicate a minimum depletion of 0.735 million tons, expected to occur in the year 2100 (7/n).

I exploited a nested constant elasticity-of-substitution production function, allowing for easy substitution between capital and natural resource depletion (6/n).

The present paper derives a closed form formula of the optimal relationship between sustainable development and natural resource depletion. This general model is structural in its nature and builds on the principles of economic theory (5/n).

The literature is divided with mitigated findings on the link between natural resource exhaustion and sustainable development, with disagreement on how to best manage natural resources in a sustainable manner as the economic approach is still the dominant model (4/n).

The scarcity of natural resources and their endowment have been proven to affect sustainable development (3/n).