Benedikt Höltgen, Robert C. Williamson
arXiv 24 Jul 2024 · Statistics — Methodology
arXiv:2407.17385 · PDF · DOI · OpenAlex · Extracted main text
The standard approach to causal modelling especially in social and health sciences is the potential outcomes framework due to Neyman and Rubin. In this framework, observations are thought to be drawn from a distribution over variables of interest, and the goal is to identify parameters of this distribution. Even though the stated goal is often to inform decision making on some target population, there is no straightforward way to include these target populations in the framework. Instead of modelling the relationship between the observed sample and the target population, the inductive assumptions in this framework take the form of abstract sampling and independence assumptions. In this paper, we develop a version of this framework that construes causal inference as treatment-wise predictions for finite populations where all assumptions are testable in retrospect; this means that one can not only test predictions themselves (without any fundamental problem) but also investigate sources of error when they fail. Due to close connections to the original framework, established methods can still be be analysed under the new framework.
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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 | Rubin, Donald B (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. | 0.874 | 5 | 2 | 100% |
| 2 | Heckman, James J, Pinto, Rodrigo (2024) Econometric causality: The central role of thought experiments | 0.843 | 3 | 3 | 100% |
| 3 | Imbens, Guido W (2020) Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics | 0.811 | 4 | 2 | 100% |
| 4 | Deaton, Angus, Cartwright, Nancy (2018) Understanding and misunderstanding randomized controlled trials | 0.737 | 3 | 3 | 67% |
| 5 | Meng, Xiao-Li (2022) Comments on "Statistical inference with non-probability survey samples"-Miniaturizing data defect correlation: A versatile strat… | 0.737 | 3 | 3 | 67% |
| 6 | Berk, Richard A (1987) Causal inference as a prediction problem | 0.737 | 3 | 2 | 100% |
| 7 | Egami, Naoki, Hartman, Erin (2023) Elements of external validity: Framework, design, and analysis | 0.737 | 3 | 2 | 100% |
| 8 | Manski, Charles F (2004) Statistical treatment rules for heterogeneous populations | 0.737 | 3 | 2 | 100% |
| 9 | Angrist, Joshua D, Pischke, Jörn-Steffen (2010) The credibility revolution in empirical economics: How better research design is taking the con out of econometrics | 0.644 | 2 | 2 | 100% |
| 10 | Cartwright, Nancy (1999) The limits of exact science, from economics to physics | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 77 scored citations.