arXiv 23 May 2018 · Econometrics · publishedJournal of Econometrics (2020) · 28 citations (OpenAlex)
arXiv:1805.09397 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a nonparametric model that represents how sequences of outcomes and treatment choices influence one another in a dynamic manner. In this setting, we are interested in identifying the average outcome for individuals in each period, had a particular treatment sequence been assigned. The identification of this quantity allows us to identify the average treatment effects (ATE's) and the ATE's on transitions, as well as the optimal treatment regimes, namely, the regimes that maximize the (weighted) sum of the average potential outcomes, possibly less the cost of the treatments. The main contribution of this paper is to relax the sequential randomization assumption widely used in the biostatistics literature by introducing a flexible choice-theoretic framework for a sequence of endogenous treatments. We show that the parameters of interest are identified under each period's two-way exclusion restriction, i.e., with instruments excluded from the outcome-determining process and other exogenous variables excluded from the treatment-selection process. We also consider partial identification in the case where the latter variables are not available. Lastly, we extend our results to a setting where treatments do not appear in every period.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Heckman, J. J. and S. Navarro (2007) Dynamic discrete choice and dynamic treatment effects | 1.000 | 7 | 4 | 100% |
| 2 | Vytlacil, E. and N. Yildiz (2007) Dummy endogenous variables in weakly separable models | 1.000 | 6 | 3 | 100% |
| 3 | Heckman, J. J., J. E. Humphries, and G. Veramendi (2016) Dynamic treatment effects | 1.000 | 5 | 3 | 100% |
| 4 | Murphy, S. A., M. J. van der Laan, J. M. Robins, and C. P. P. R. Group (2001) Marginal mean models for dynamic regimes | 1.000 | 5 | 3 | 100% |
| 5 | Balat, J. and S. Han (2018) Multiple treatments with strategic interaction | 0.843 | 3 | 3 | 100% |
| 6 | Murphy, S. A (2003) Optimal dynamic treatment regimes | 0.811 | 4 | 2 | 100% |
| 7 | Vikström, J., G. Ridder, and M. Weidner (2018) Bounds on treatment effects on transitions | 0.811 | 4 | 2 | 100% |
| 8 | Abbring, J. H. and J. J. Heckman (2007) Econometric evaluation of social programs, part III: Distributional treatment effects, dynamic treatment effects, dynamic discre… | 0.644 | 2 | 2 | 100% |
| 9 | Abraham, S. and L. Sun (2018) Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects | 0.644 | 2 | 2 | 100% |
| 10 | Callaway, B. and P. H. Sant'Anna (2018) Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment | 0.644 | 2 | 2 | 100% |
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