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Dynamic covariate balancing: estimating treatment effects over time with potential local projections

Davide Viviano, Jelena Bradic

arXiv 1 Mar 2021 · Econometrics · 6 citations (OpenAlex)

arXiv:2103.01280 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper studies the estimation and inference of treatment histories in panel data settings when treatments change dynamically over time. We propose a method that allows for (i) treatments to be assigned dynamically over time based on high-dimensional covariates, past outcomes and treatments; (ii) outcomes and time-varying covariates to depend on treatment trajectories; (iii) heterogeneity of treatment effects. Our approach recursively projects potential outcomes' expectations on past histories. It then controls the bias by balancing dynamically observable characteristics. We study the asymptotic and numerical properties of the estimator and illustrate the benefits of the procedure in an empirical application.

Citation extraction

72
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Acemoglu, D., S. Naidu, P. Restrepo, and J. A. Robinson (2019) Democracy does cause growth1.000183100%
2Robins, J. M., M. A. Hernan, and B. Brumback (2000) Marginal structural models and causal inference in epidemiology0.9507586%
3Nie, X., E. Brunskill, and S. Wager (2021) Learning when-to-treat policies0.9416483%
4Tchetgen, E. J. T. and I. Shpitser (2012) Semiparametric theory for causal mediation analysis: efficiency bounds, multiple robustness, and sensitivity analysis0.8746467%
5Jordà, Ò (2005) Estimation and inference of impulse responses by local projections0.8435360%
6Imai, K. and M. Ratkovic (2015) Robust estimation of inverse probability weights for marginal structural models0.84333100%
7Athey, S., G. W. Imbens, and S. Wager (2018) Approximate residual balancing: debiased inference of average treatment effects in high dimensions0.83612858%
8Montiel Olea, J. L. and M. Plagborg-Mller (2021) Local projection inference is simpler and more robust than you think0.7374350%
9Ghanem, D., P. H. Sant'Anna, and K. Wüthrich (2022) Selection and parallel trends0.7373367%
10Jiang, N. and L. Li (2015) Doubly robust off-policy value evaluation for reinforcement learning0.7373367%

Showing the top 10 of 72 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
12606.290090.64422
2When are time series predictions causal? The potential system and dynamic causal effects0.40511
3What is the Long-Term Value of Reliability?0.40511