Harsh Parikh, Carlos Varjao, Louise Xu, Eric Tchetgen Tchetgen
arXiv 9 Feb 2022 · Statistics — Methodology · 10 citations (OpenAlex)
arXiv:2202.04208 · PDF · DOI · OpenAlex · Extracted main text
The fundamental challenge of drawing causal inference is that counterfactual outcomes are not fully observed for any unit. Furthermore, in observational studies, treatment assignment is likely to be confounded. Many statistical methods have emerged for causal inference under unconfoundedness conditions given pre-treatment covariates, including propensity score-based methods, prognostic score-based methods, and doubly robust methods. Unfortunately for applied researchers, there is no `one-size-fits-all' causal method that can perform optimally universally. In practice, causal methods are primarily evaluated quantitatively on handcrafted simulated data. Such data-generative procedures can be of limited value because they are typically stylized models of reality. They are simplified for tractability and lack the complexities of real-world data. For applied researchers, it is critical to understand how well a method performs for the data at hand. Our work introduces a deep generative model-based framework, Credence, to validate causal inference methods. The framework's novelty stems from its ability to generate synthetic data anchored at the empirical distribution for the observed sample, and therefore virtually indistinguishable from the latter. The approach allows the user to specify ground truth for the form and magnitude of causal effects and confounding bias as functions of covariates. Thus simulated data sets are used to evaluate the potential performance of various causal estimation methods when applied to data similar to the observed sample. We demonstrate Credence's ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications from Lalonde and Project STAR studies.
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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 | P. Langley (2000) Crafting Papers on Machine Learning | 0.737 | 3 | 2 | 100% |
| 2 | A. L. Samuel (1959) Some Studies in Machine Learning Using the Game of Checkers | 0.644 | 4 | 1 | 100% |
| 3 | (1983) Machine Learning: An Artificial Intelligence Approach, Vol. I | 0.511 | 2 | 1 | 100% |
| 4 | M. J. Kearns (1989) Computational Complexity of Machine Learning | 0.511 | 2 | 1 | 100% |
| 5 | T. M. Mitchell (1980) The Need for Biases in Learning Generalizations | 0.511 | 2 | 1 | 100% |
| 6 | R. O. Duda and P. E. Hart and D. G. Stork (2000) Pattern Classification | 0.405 | 1 | 1 | 100% |
| 7 | A. Newell and P. S. Rosenbloom (1981) Mechanisms of Skill Acquisition and the Law of Practice | 0.405 | 1 | 1 | 100% |
| 8 | Author, N. N (2021) Suppressed for Anonymity | 0.405 | 1 | 1 | 100% |
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