Anish Agarwal, Sukjin Han, Dwaipayan Saha, Vasilis Syrgkanis, Haeyeon Yoon
arXiv 20 Oct 2022 · Econometrics
arXiv:2210.11003 · PDF · DOI · OpenAlex · Extracted main text
We propose a generalization of the synthetic control and interventions methods to the setting with dynamic treatment effects. We consider the estimation of unit-specific treatment effects from panel data collected under a general treatment sequence. Here, each unit receives multiple treatments sequentially, according to an adaptive policy that depends on a latent, endogenously time-varying confounding state. Under a low-rank latent factor model assumption, we develop an identification strategy for any unit-specific mean outcome under any sequence of interventions. The latent factor model we propose admits linear time-varying and time-invariant dynamical systems as special cases. Our approach can be viewed as an identification strategy for structural nested mean models -- a widely used framework for dynamic treatment effects -- under a low-rank latent factor assumption on the blip effects. Unlike these models, however, it is more permissive in observational settings, thereby broadening its applicability. Our method, which we term synthetic blip effects, is a backwards induction process in which the blip effect of a treatment at each period and for a target unit is recursively expressed as a linear combination of the blip effects of a group of other units that received the designated treatment. This strategy avoids the combinatorial explosion in the number of units that would otherwise be required by a naive application of prior synthetic control and intervention methods in dynamic treatment settings. We provide estimation algorithms that are easy to implement in practice and yield estimators with desirable properties. Using unique Korean firm-level panel data, we demonstrate how the proposed framework can be used to estimate individualized dynamic treatment effects and to derive optimal treatment allocation rules in the context of financial support for exporting firms.
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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 | Paravisini, D., Rappoport, V., Schnabl, P., and Wolfenzon, D (2015) Dissecting the effect of credit supply on trade: Evidence from matched credit-export data | 0.737 | 3 | 2 | 100% |
| 2 | Agarwal, A., Shah, D., and Shen, D (2020) Synthetic interventions self | 0.717 | 19 | 5 | 37% |
| 3 | Amiti, M. and Weinstein, D. E (2011) Exports and financial shocks | 0.644 | 2 | 2 | 100% |
| 4 | Chor, D. and Manova, K (2012) Off the cliff and back? credit conditions and international trade during the global financial crisis | 0.644 | 2 | 2 | 100% |
| 5 | Agarwal, A., Shah, D., and Shen, D (2020) On principal component regression in a high-dimensional error-in-variables setting self | 0.644 | 2 | 2 | 100% |
| 6 | Agarwal, A., Shah, D., Shen, D., and Song, D (2021) On robustness of principal component regression self | 0.644 | 2 | 2 | 100% |
| 7 | Abadie, A. and Gardeazabal, J (2003) The economic costs of conflict: A case study of the basque country | 0.511 | 2 | 1 | 100% |
| 8 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of californiaâs tobacco control program | 0.511 | 2 | 1 | 100% |
| 9 | Hsiao, C., Steve Ching, H., and Ki Wan, S (2012) A panel data approach for program evaluation: Measuring the benefits of political and economic integration of hong kong with mai… | 0.405 | 1 | 1 | 100% |
| 10 | Chan, M. K. and Kwok, S (2020) The PCDID Approach: Difference-in-Differences when Trends are Potentially Unparallel and Stochastic | 0.405 | 1 | 1 | 100% |
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