5.1k citations · 5.5k across the 15 of their papers we have counts for
17 papers
Using Pre-Trends for Inference in Difference-in-Differences
Clément de Chaisemartin
Difference-in-differences (DID) are sometimes estimated with many pre-treatment periods. In such settings, the observed pre-treatment outcome evolutions provide direct information…
Randomly Assigned First Differences?
Facundo Argañaraz, Clément de Chaisemartin, Ziteng Lei
We consider a first-difference regression of an outcome evolution on a treatment evolution . If the treatment effect changes over time, the regression residual is a functi…
Is Inference Conditional on Not Rejecting a Pre-test Less Reliable than Unconditional Inference?
Clément de Chaisemartin, Xavier D'Haultfœuille
Researchers often report an estimate and confidence interval only if a specification test does not reject - for example, only if a pre-trend test supports a difference-in-differenc…
Estimating treatment-effect heterogeneity across sites, in multi-site randomized experiments with few units per site
Clément de Chaisemartin, Antoine Deeb
Multi-site randomized experiments, which are common in economics and medicine, can reveal how treatment effects vary across settings. However, differences between site-specific est…
Difference-in-Differences Estimators When No Unit Remains Untreated
Clément de Chaisemartin, Diego Ciccia, Xavier D'Haultfœuille +1
We study difference-in-differences designs in which all units are initially untreated and later receive heterogeneous, strictly positive treatment doses. When quasi-untreated units…
Difference-in-Differences Estimators with Continuous Treatments and no Stayers
Clément de Chaisemartin, Xavier D'Haultfœuille, Gonzalo Vazquez-Bare
Many treatments or policy interventions are continuous in nature. Examples include prices, taxes or temperatures. Empirical researchers have usually relied on two-way fixed effect…
Difference-in-Differences Estimators for Treatments Continuously Distributed at Every Period
Clément de Chaisemartin, Xavier D'Haultfœuille, Félix Pasquier +2
When studying the effects of taxes, tariffs, or prices using panel data, the treatment is often continuously distributed in every period. We develop difference-in-differences (DID)…
Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey
Clément de Chaisemartin, Xavier D'Haultfœuille
Linear regressions with period and group fixed effects are widely used to estimate policies' effects: 26 of the 100 most cited papers published by the American Economic Review from…
Trading-off Bias and Variance When the Size of the Treatment Effect is Bounded
Clément de Chaisemartin
Assume that one is interested in estimating an average treatment effect (ATE), equal to a weighted average of conditional average treatment effects (CATEs). One has unbiased es…
More Robust Estimators for Instrumental-Variable Panel Designs, With An Application to the Effect of Imports from China on US Employment
Clément de Chaisemartin, Ziteng Lei
We show that first-difference two-stages-least-squares regressions identify non-convex combinations of location-and-period-specific treatment effects. Thus, those regressions could…
Two-way Fixed Effects and Differences-in-Differences Estimators with Several Treatments
Clément de Chaisemartin, Xavier D'Haultfœuille
We study two-way-fixed-effects regressions (TWFE) with several treatment variables. Under a parallel trends assumption, we show that the coefficient on each treatment identifies a…
Difference-in-Differences Estimators of Intertemporal Treatment Effects
Clément de Chaisemartin, Xavier D'Haultfœuille
We study treatment-effect estimation using panel data. The treatment may be non-binary, non-absorbing, and the outcome may be affected by treatment lags. We make a parallel-trends…
Clustering and External Validity in Randomized Controlled Trials
Antoine Deeb, Clément de Chaisemartin
The randomization inference literature studying randomized controlled trials (RCTs) assumes that units' potential outcomes are deterministic. This assumption is unlikely to hold, a…
At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?
Clément de Chaisemartin, Jaime Ramirez-Cuellar
In matched-pairs experiments in which one cluster per pair of clusters is assigned to treatment, to estimate treatment effects, researchers often regress their outcome on a treatme…
Two-way fixed effects estimators with heterogeneous treatment effects
Clément de Chaisemartin, Xavier D'Haultfœuille
Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they estimate weighted sums of the average treatment effects (ATE…
Estimating the effect of treatments allocated by randomized waiting lists
Clement de Chaisemartin, Luc Behaghel
Oversubscribed treatments are often allocated using randomized waiting lists. Applicants are ranked randomly, and treatment offers are made following that ranking until all seats a…
Fuzzy Differences-in-Differences
Clement de Chaisemartin, Xavier D'Haultfoeuille
Difference-in-differences (DID) is a method to evaluate the effect of a treatment. In its basic version, a "control group" is untreated at two dates, whereas a "treatment group" be…