Ravi B. Sojitra, Vasilis Syrgkanis
arXiv 2 May 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2405.01463 · PDF · DOI · OpenAlex · Extracted main text
We consider Dynamic Treatment Regimes (DTRs) with One Sided Noncompliance that arise in applications such as digital recommendations and adaptive medical trials. These are settings where decision makers encourage individuals to take treatments over time, but adapt encouragements based on previous encouragements, treatments, states, and outcomes. Importantly, individuals may not comply with encouragements based on unobserved confounders. For settings with binary treatments and encouragements, we provide nonparametric identification, estimation, and inference for Dynamic Local Average Treatment Effects (LATEs), which are expected values of multiple time period treatment effect contrasts for the respective complier subpopulations. Under One Sided Noncompliance and sequential extensions of the assumptions in Imbens and Angrist (1994), we show that one can identify Dynamic LATEs that correspond to treating at single time steps. In Staggered Adoption settings, we show that the assumptions are sufficient to identify Dynamic LATEs for treating in multiple time periods. Moreover, this result extends to any setting where the effect of a treatment in one period is uncorrelated with the compliance event in a subsequent 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 | Guido Imbens and Joshua Angrist (1994) Identification and estimation of local average treatment effects | 1.000 | 8 | 5 | 100% |
| 2 | James Robins (1986) A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy w… | 1.000 | 6 | 4 | 100% |
| 3 | Victor Chernozhukov, Whitney Newey, Rahul Singh, and Vasilis Syrgkanis (2022) Automatic debiased machine learning for dynamic treatment effects and general nested functionals self | 0.935 | 11 | 4 | 82% |
| 4 | Sukjin Han (2021) Identification in nonparametric models for dynamic treatment effects | 0.874 | 6 | 2 | 100% |
| 5 | James J Heckman and Salvador Navarro (2007) Dynamic discrete choice and dynamic treatment effects | 0.874 | 6 | 2 | 100% |
| 6 | James J Heckman, John Eric Humphries, and Gregory Veramendi (2016) Dynamic treatment effects | 0.874 | 6 | 2 | 100% |
| 7 | Ruth Miquel (2002) Identification of dynamic treatment effects by instrumental variables | 0.874 | 6 | 2 | 100% |
| 8 | Shuxiao Chen and Bo Zhang (2023) Estimating and improving dynamic treatment regimes with a time-varying instrumental variable | 0.874 | 5 | 2 | 100% |
| 9 | Sukjin Han (2023) Optimal dynamic treatment regimes and partial welfare ordering | 0.874 | 5 | 2 | 100% |
| 10 | Y Cui, H Michael, F Tanser, and E Tchetgen Tchetgen (2023) Instrumental variable estimation of the marginal structural cox model for time-varying treatments | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 53 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects | 0.405 | 1 | 1 |
| 2 | Dynamic Local Average Treatment Effects in Time Series | 0.405 | 1 | 1 |