IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2601.04223.html

Beyond Interaction Effects: Two Logics for Studying Population Inequalities

Author

Listed:
  • Adel Daoud

Abstract

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.

Suggested Citation

  • Adel Daoud, 2025. "Beyond Interaction Effects: Two Logics for Studying Population Inequalities," Papers 2601.04223, arXiv.org.
    • Handle: RePEc:arx:papers:2601.04223
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2601.04223
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ian Lundberg, 2024. "The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories," Sociological Methods & Research, , vol. 53(2), pages 507-570, May.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Victor Chernozhukov & Christian Hansen & Nathan Kallus & Martin Spindler & Vasilis Syrgkanis, 2024. "Applied Causal Inference Powered by ML and AI," Papers 2403.02467, arXiv.org.
    4. Daoud, Adel & Reinsberg, Bernhard & Kentikelenis, Alexander E. & Stubbs, Thomas H. & King, Lawrence P., 2019. "The International Monetary Fund’s interventions in food and agriculture: An analysis of loans and conditions," Food Policy, Elsevier, vol. 83(C), pages 204-218.
    5. Sourabh Balgi & Adel Daoud & Jose M. Peña & Geoffrey T. Wodtke & Jesse Zhou, 2025. "Deep Learning With DAGs," Sociological Methods & Research, , vol. 54(4), pages 1624-1682, November.
    6. Brambor, Thomas & Clark, William Roberts & Golder, Matt, 2006. "Understanding Interaction Models: Improving Empirical Analyses," Political Analysis, Cambridge University Press, vol. 14(1), pages 63-82, January.
    7. X Nie & S Wager, 2021. "Quasi-oracle estimation of heterogeneous treatment effects [TensorFlow: A system for large-scale machine learning]," Biometrika, Biometrika Trust, vol. 108(2), pages 299-319.
    8. Sourabh Balgi & Jose M. Pe~na & Adel Daoud, 2022. "Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows," Papers 2202.09391, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.
    2. Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.
    3. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    4. Wu, Guojun & Song, Ge & Lv, Xiaoxiang & Luo, Shikai & Shi, Chengchun & Zhu, Hongtu, 2023. "DNet: distributional network for distributional individualized treatment effects," LSE Research Online Documents on Economics 122895, London School of Economics and Political Science, LSE Library.
    5. Ta-Wei Huang & Eva Ascarza, 2024. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals," Marketing Science, INFORMS, vol. 43(4), pages 863-884, July.
    6. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    7. Bolbocean, Corneliu & Anderson, Peter J. & Bartmann, Peter & Cheong, Jeanie L.Y. & Doyle, Lex W. & Johnson, Samantha & Marlow, Neil & Wolke, Dieter & Petrou, Stavros & O'Neill, Stephen, 2025. "A heterogeneity analysis of health-related quality of life in early adults born very preterm or very low birthweight across the sociodemographic spectrum," Social Science & Medicine, Elsevier, vol. 380(C).
    8. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
    9. Jun Mao & Jiahao Xie & Zunguo Hu & Lijie Deng & Haitao Wu & Yu Hao, 2023. "Sustainable development through green innovation and resource allocation in cities: Evidence from machine learning," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(4), pages 2386-2401, August.
    10. Hua Chen & Jianing Xing & Xiaoxu Yang & Kai Zhan, 2021. "Heterogeneous Effects of Health Insurance on Rural Children’s Health in China: A Causal Machine Learning Approach," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
    11. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    12. Artem Timoshenko & Caio Waisman, 2025. "Profit-Aligned CATE Estimation: Reconciling Policy Learning and Inference," Papers 2512.13400, arXiv.org, revised Apr 2026.
    13. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
    14. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.
    15. Weicong Lyu & Jee-Seon Kim & Youmi Suk, 2023. "Estimating Heterogeneous Treatment Effects Within Latent Class Multilevel Models: A Bayesian Approach," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 3-36, February.
    16. Alicia Curth & Mihaela van der Schaar, 2023. "In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation," Papers 2302.02923, arXiv.org, revised Jun 2023.
    17. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    18. Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
    19. Patrick Rehill & Nicholas Biddle, 2025. "Policy Learning for Many Outcomes of Interest: Combining Optimal Policy Trees with Multi-objective Bayesian Optimisation," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 971-1001, August.
    20. Retsef Levi & Elisabeth Paulson & Georgia Perakis & Emily Zhang, 2024. "Heterogeneous Treatment Effects in Panel Data," Papers 2406.05633, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2601.04223. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.