Keyword

econometrics

3 papers tagged “econometrics”

EconomicsJournal of Econometrics · Dec 2021 Open access

Difference-in-Differences with Multiple Time Periods

Brantly Callaway and Pedro H. C. Sant'Anna

The paper develops a framework for difference-in-differences designs in which units adopt treatment at different times across multiple periods. It defines group-time average treatment effects and shows how to identify and estimate them under conditional parallel trends, then aggregate them into interpretable summary parameters. The authors provide valid simultaneous inference and apply the methods to estimating the effect of minimum wage increases on teen employment.

difference-in-differences staggered-treatment causal-inference parallel-trends

EconomicsAmerican Economic Review · Sept 2020

Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects

Clément de Chaisemartin and Xavier D'Haultfœuille

The paper shows that linear regressions with group and period fixed effects estimate a weighted sum of treatment effects across cells where some weights can be negative. As a consequence the coefficient can be negative even when every cell-level effect is positive, undermining its interpretation as an average treatment effect. The authors propose an alternative estimator (DID_M) that is robust to heterogeneous effects and assess the empirical relevance of the negative-weighting problem.

two-way-fixed-effects heterogeneous-treatment-effects difference-in-differences panel-data

EconomicsNBER Working Paper Series (No. 24678) · Jun 2018 Open access

Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India

Victor Chernozhukov, Mert Demirer, Esther Duflo and Iván Fernández-Val

The paper develops a generic method to use any machine learning algorithm to draw valid statistical inference about features of heterogeneous treatment effects in randomized experiments. Rather than estimating the conditional average treatment effect function itself (which ML may estimate inconsistently), it targets summary parameters such as the best linear predictor of the effect on ML proxies, sorted average effects across groups, and average characteristics of the most/least affected units, using sample splitting and aggregation over many splits to obtain robust confidence intervals. It illustrates the approach with an immunization study in India.

machine learning heterogeneous treatment effects causal inference randomized experiments