#DifferenceInDifferences

Dominik Liebl

@dliebl.com

1 month ago

Making Event Study Plots Honest: A Functional Data Approach to Causal Inference Event study plots are the centerpiece of Difference-in-Differences (DiD) analysis, but current plotting methods cannot provide honest causal inference when the parallel trends and/or no-anticipation a...

📄 Paper: arxiv.org/abs/2512.06804
📦 R package: ccfang2.github.io/fdid/

Joint work with my PhD student Chencheng Fang — he did a fantastic job.
▶️ ccfang2.github.io

#CausalInference #DifferenceInDifferences #Econometrics #EventStudy #Statistics #RStats #AcademicResearch

@arxivlens.bsky.social

4 months ago

Bias-Variance Tradeoff of Matching Prior to Difference-in-Differences
When Parallel Trends is Violated
Dae Woong Ham, Mingxuan Ge
Paper
Details
#BiasVarianceTradeoff #DifferenceInDifferences #ParallelTrendsViolation

LISER - Luxembourg Institute of Socio-Economic Research

@liser.lu

10 months ago

📽️ Watch a 12 hour course by Prof. @cdechaisemartin.bsky.social on #DifferenceInDifferences (DiD), hosted at LISER.

💡 The course begins w/ a review of fundamental concepts & progresses to advanced techniques, emphasizing the latest innovations in DiD revolution.

🔗 www.youtube.com/playlist?lis...

Ninon Moreau-Kastler

@nmoreaukastler.bsky.social

10 months ago

… Stata command coming soon ! 👩‍💻

9/9

#EconBluesky #Econometrics #Differenceindifferences

Martin Huber

@causalhuber.bsky.social

1 year ago

Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning We propose a difference-in-differences (DiD) method for a time-varying continuous treatment and multiple time periods. Our framework assesses the average treatment effect on the treated (ATET) when co...

😀 New paper alert! Check out our #DifferenceInDifferences method for time-varying continuous treatments based on double #MachineLearning and kernel methods - joint work with Michel Haddad and Lucas Zhang: arxiv.org/abs/2410.21105 #CausalAnalysis

Martin Huber

@causalhuber.bsky.social

1 year ago

On the testability of common trends in panel data without placebo periods We demonstrate and discuss the testability of the common trend assumption imposed in Difference-in-Differences (DiD) estimation in panel data when not relying on multiple pre-treatment periods for...

🔥 #EconSky Check out my new paper on #DifferenceInDifferences, exploring the testability of the common trend assumption in panel data without requiring multiple pre-treatment periods for placebo tests: arxiv.org/abs/2404.16961

Martin Huber

@causalhuber.bsky.social

2 years ago

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: Our working paper on #MachineLearning for staggered #DifferenceInDifferences is out! Our method explores effect heterogeneity under staggered treatment adoption and is applied to assess the effect of Brazil's Family Health Program on infant mortality: arxiv.org/abs/2310.11962