mlsanityData to 14 Oct 2026

Beyond Interaction Effects: Two Logics for Studying Population Inequalities

arXiv 2601.04223 ↗First posted 26 Dec 2025cs.CY cs.AI cs.LGcited by 0

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Abstract (arXiv)

When sociologists and other social scientist ask whether the return to college differs by race and gender, they face a choice between two fundamentally different modes of inquiry. Traditional interaction models follow deductive logic: the researcher specifies which variables moderate effects and tests these hypotheses. Machine learning methods follow inductive logic: algorithms search across vast combinatorial spaces to discover patterns of heterogeneity. This article develops a framework for navigating between these approaches. We show that the choice between deduction and induction reflects a tradeoff between interpretability and flexibility, and we demonstrate through simulation when each approach excels. Our framework is particularly relevant for inequality research, where understanding how treatment effects vary across intersecting social subpopulation is substantively central.

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References · 18

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  3. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 332
  4. 2016Generalized Random Forestscited by 312
  5. 2024Applied Causal Inference Powered by ML and AIcited by 30
  6. 2022Image-based Treatment Effect Heterogeneitycited by 20
  7. 2020Statistical modeling: the three culturescited by 18
  8. 2022Personalized Public Policy Analysis in Social Sciences using Causal-Graphical Normalizing Flowscited by 16
  9. 2021On the Distinction Between "Conditional Average Treatment Effects" (CATE) and "Individual Treatment Effects" (ITE) Under Ignorability Assumptionscited by 9
  10. 2024Deep Learning With DAGscited by 7
  11. 2025Benchmarking Debiasing Methods for LLM-based Parameter Estimatescited by 6
  12. 2024A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Povertycited by 5
6 more
  1. 2022Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africacited by 4
  2. 2024Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTscited by 4
  3. 2022Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flowscited by 3
  4. 2022ρ-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flowscited by 2
  5. 2023Navigating Unmeasured Confounding in Quantitative Sociology: A Sensitivity Frameworkcited by 1
  6. 2025Sensitivity Analysis to Unobserved Confounding with Copula-based Normalizing Flowscited by 1

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