Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis
arXiv 15 Mar 2021 · Econometrics · 2 citations (OpenAlex)
arXiv:2103.08390 · PDF · DOI · OpenAlex · Extracted main text
Policy makers typically face the problem of wanting to estimate the long-term effects of novel treatments, while only having historical data of older treatment options. We assume access to a long-term dataset where only past treatments were administered and a short-term dataset where novel treatments have been administered. We propose a surrogate based approach where we assume that the long-term effect is channeled through a multitude of available short-term proxies. Our work combines three major recent techniques in the causal machine learning literature: surrogate indices, dynamic treatment effect estimation and double machine learning, in a unified pipeline. We show that our method is consistent and provides root-n asymptotically normal estimates under a Markovian assumption on the data and the observational policy. We use a data-set from a major corporation that includes customer investments over a three year period to create a semi-synthetic data distribution where the major qualitative properties of the real dataset are preserved. We evaluate the performance of our method and discuss practical challenges of deploying our formal methodology and how to address them.
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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 | Athey, S., Chetty, R., Imbens, G., and Kang, H (2020) Estimating treatment effects using multiple surrogates: The role of the surrogate score and the surrogate index, 2020 | 1.000 | 9 | 4 | 100% |
| 2 | Lewis, G. and Syrgkanis, V (2020) Double/debiased machine learning for dynamic treatment effects, 2020 self | 0.956 | 8 | 5 | 88% |
| 3 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.855 | 8 | 5 | 62% |
| 4 | Robins, J. M (2004) Optimal Structural Nested Models for Optimal Sequential Decisions, pp.\ 189–326 | 0.693 | 6 | 1 | 100% |
| 5 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.644 | 2 | 2 | 100% |
| 6 | Vansteelandt, S., Joffe, M., et al (2014) Structural nested models and g-estimation: the partially realized promise | 0.644 | 2 | 2 | 100% |
| 7 | Chakraborty, B. and Moodie, E. E. M (2013) Semi-parametric Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes, pp.\ 53–78 | 0.511 | 2 | 1 | 100% |
| 8 | van der Laan, M. J. and Gruber, S (2011) Targeted minimum loss based estimation of an intervention specific mean outcome | 0.511 | 2 | 1 | 100% |
| 9 | Ai, C. and Chen, X (2003) Efficient estimation of models with conditional moment restrictions containing unknown functions | 0.405 | 1 | 1 | 100% |
| 10 | Chatterjee, S. and Bose, A (2005) Generalized bootstrap for estimating equations | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Kernel methods for long term dose response curves | 0.405 | 1 | 1 |
| 2 | Long-term Causal Inference Under Persistent Confounding via Data Combination | 0.405 | 1 | 1 |
| 3 | Estimating Effects of Long-Term Treatments | 0.405 | 1 | 1 |
| 4 | What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness | 0.405 | 1 | 1 |