Causal Feature Learning in the Social Sciences
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Abstract (arXiv)
Variable selection poses a significant challenge in causal modeling, particularly within the social sciences, where constructs often rely on inter-related factors such as age, socioeconomic status, gender, and race. Indeed, it has been argued that such attributes must be modeled as macro-level abstractions of lower-level manipulable features, in order to preserve the modularity assumption essential to causal inference. This paper accordingly extends the theoretical framework of Causal Feature Learning (CFL). Empirically, we apply the CFL algorithm to diverse social science datasets, evaluating how CFL-derived macrostates compare with traditional microstates in downstream modeling tasks.
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References · 13
- 2017Counterfactual Fairnesscited by 804
- 2023Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretabilitycited by 205
- 2017Avoiding Discrimination through Causal Reasoningcited by 194
- 2019Approximate Causal Abstractioncited by 63
- 2014Visual Causal Feature Learningcited by 59
- 2022Causal Fairness Analysiscited by 43
- 2020What's Sex Got To Do With Fair Machine Learning?cited by 38
- 2021Compositional Abstraction Error and a Category of Causal Modelscited by 33
- 2015Multi-Level Cause-Effect Systemscited by 30
- 2016Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Datacited by 20
- 2023Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractionscited by 11
- 2021A Topological Perspective on Causal Inferencecited by 10
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- 2025Modeling Discrimination with Causal Abstractioncited by 2
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