Panel experiments and dynamic causal effects: A finite population perspective
By Iavor Bojinov, Ashesh Rambachan, N. Shephard. Quantitative Economics, 2020
57 citations recorded by the source corpus.
In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative effectiveness of alternative treatment paths. For a rich class of dynamic causal effects, we provide a nonparametric estimator that is unbiased over the randomization distribution and derive its finite population limiting distribution as either the sample size or the duration of the experiment increases. We develop two methods for inference: a conservative test for weak null hypotheses and an exact randomization test for sharp null hypotheses. We further analyze the finite population probability limit of linear fixed effects estimators. These commonly‐used estimators do not recover a causally interpretable estimand if there are dynamic causal effects and serial correlation in the assignments, highlighting the value of our proposed estimator.
Subfields: Education, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/39bf36260ebef5c2e43965219e81fc683a0750a7
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Citations in the corpus
- Difference-in-Differences Estimators of Intertemporal Treatment Effects (2020, unknown): “Finally, another related paper is Bojinov et al. (2020), who propose estimators of instantaneous and dynamic treatment effects, in panel experiments.”
- What’s trending in difference-in-differences? A synthesis of the recent econometrics literature (2022, unknown): “We note that, as described in Bojinov et al. (2021), the no carryover assumption may be restrictive in many settings — for example, if the treatment is a raise in the minimum wage and the outcome is employment, then the no carryover assumption requires that employment in period t depends only on…”
- Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey (2021, background): “This excludes papers that have assumed randomized treatment timing (see, e.g., Athey and Imbens, 2022; Roth and Sant’Anna, 2021) or sequential treatment randomization (see, e.g., Bojinov et al., 2021), rather than parallel trends.”