Shantanu Gupta, Zachary C. Lipton, David Childers
arXiv 20 Aug 2021 · Machine Learning · 1 citations (OpenAlex)
arXiv:2108.09265 · PDF · DOI · OpenAlex · Extracted main text
Researchers often face data fusion problems, where multiple data sources are available, each capturing a distinct subset of variables. While problem formulations typically take the data as given, in practice, data acquisition can be an ongoing process. In this paper, we aim to estimate any functional of a probabilistic model (e.g., a causal effect) as efficiently as possible, by deciding, at each time, which data source to query. We propose online moment selection (OMS), a framework in which structural assumptions are encoded as moment conditions. The optimal action at each step depends, in part, on the very moments that identify the functional of interest. Our algorithms balance exploration with choosing the best action as suggested by current estimates of the moments. We propose two selection strategies: (1) explore-then-commit (OMS-ETC) and (2) explore-then-greedy (OMS-ETG), proving that both achieve zero asymptotic regret as assessed by MSE. We instantiate our setup for average treatment effect estimation, where structural assumptions are given by a causal graph and data sources may include subsets of mediators, confounders, and instrumental variables.
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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 | J. D. Angrist (1990) Lifetime earnings and the vietnam era draft lottery: evidence from social security administrative records | 0.737 | 4 | 2 | 75% |
| 2 | J. L. Hill (2011) Bayesian nonparametric modeling for causal inference | 0.737 | 3 | 2 | 100% |
| 3 | L. P. Hansen (1982) Large sample properties of generalized method of moments estimators | 0.644 | 2 | 2 | 100% |
| 4 | W. K. Newey and D. McFadden (1994) Large sample estimation and hypothesis testing | 0.511 | 5 | 2 | 20% |
| 5 | R. Zhan, Z. Ren, S. Athey, and Z. Zhou (2021) Policy learning with adaptively collected data | 0.511 | 2 | 2 | 50% |
| 6 | T. Lattimore and C. Szepesvári (2020) Bandit algorithms | 0.511 | 2 | 1 | 100% |
| 7 | V. Dorie (2016) Non-parametrics for causal inference | 0.405 | 1 | 1 | 100% |
| 8 | D. W. Andrews (1999) Consistent moment selection procedures for generalized method of moments estimation | 0.405 | 1 | 1 | 100% |
| 9 | J. D. Angrist (2009) Replication data for: Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records,… | 0.405 | 1 | 1 | 100% |
| 10 | E. Bareinboim and J. Pearl (2016) Causal inference and the data-fusion problem | 0.405 | 1 | 1 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.