mlsanityData to 14 Oct 2026

Causes of Effects: Learning individual responses from population data

arXiv 2104.13730 ↗First posted 28 Apr 2021stat.ME cs.LGcited by 37

Not on the map. Why?

Abstract (arXiv)

The problem of individualization is recognized as crucial in almost every field. Identifying causes of effects in specific events is likewise essential for accurate decision making. However, such estimates invoke counterfactual relationships, and are therefore indeterminable from population data. For example, the probability of benefiting from a treatment concerns an individual having a favorable outcome if treated and an unfavorable outcome if untreated. Experiments conditioning on fine-grained features are fundamentally inadequate because we can't test both possibilities for an individual. Tian and Pearl provided bounds on this and other probabilities of causation using a combination of experimental and observational data. Even though those bounds were proven tight, narrower bounds, sometimes significantly so, can be achieved when structural information is available in the form of a causal model. This has the power to solve central problems, such as explainable AI, legal responsibility, and personalized medicine, all of which demand counterfactual logic. We analyze and expand on existing research by applying bounds to the probability of necessity and sufficiency (PNS) along with graphical criteria and practical applications.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). Builds on 3 of its 26 references that are in the map. First posted on arXiv 28 Apr 2021.

Builds on

3 of 26 references.

References · 3

  1. 2013Probabilities of Causation: Bounds and Identificationcited by 90
  2. 2013Counterfactuals and Policy Analysis in Structural Modelscited by 13
  3. 2013Psychological and Normative Theories of Causal Power and the Probabilities of Causescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 14; 2023: 5; 2024: 5; 2025: 5; 2026: 6.

37 papers.

Sort:

By area:

  1. 2021Desiderata for Representation Learning: A Causal Perspectivecited by 76
  2. 2022Personalized Decision Making -- A Conceptual Introductioncited by 30
  3. 2022Probabilities of Causation with Nonbinary Treatment and Effectcited by 24
  4. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  5. 2021Unit Selection with Causal Diagramcited by 17
  6. 2022Unit Selection with Nonbinary Treatment and Effectcited by 12
  7. 2022Counterfactual Analysis in Dynamic Latent State Modelscited by 10
  8. 2022Probabilities of Causation: Adequate Size of Experimental and Observational Samplescited by 8
  9. 2023Efficient Computation of Counterfactual Boundscited by 8
  10. 2024Probabilities of Causation for Continuous and Vector Variablescited by 8
  11. 2022On the Complexity of Counterfactual Reasoningcited by 7
  12. 2022Bounding Counterfactuals under Selection Biascited by 6
25 more
  1. 2022Unit Selection: Learning Benefit Function from Finite Population Datacited by 6
  2. 2024Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanationcited by 6
  3. 2022Unit Selection: Case Study and Comparison with A/B Test Heuristiccited by 5
  4. 2022Probabilities of Causation: Role of Observational Datacited by 5
  5. 2023Epsilon-Identifiability of Causal Quantitiescited by 4
  6. 2022Is More Data All You Need? A Causal Explorationcited by 3
  7. 2022Partial counterfactual identification and uplift modeling: theoretical results and real-world assessmentcited by 3
  8. 2023Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxiescited by 3
  9. 2023Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviourcited by 3
  10. 2022A Causal Research Pipeline and Tutorial for Psychologists and Social Scientistscited by 2
  11. 2024Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiencycited by 2
  12. 2025Estimating Probabilities of Causation with Machine Learning Modelscited by 2
  13. 2023An Algorithm and Complexity Results for Causal Unit Selectioncited by 1
  14. 2025Identification of Probabilities of Causation: from Recursive to Closed-Form Boundscited by 1
  15. 2025Learning Probabilities of Causation with Mask-Augmented Datacited by 1
  16. 2025Causal computations in Semi Markovian Structural Causal Models using divide and conquercited by 1
  17. 2026Bounding Probabilities of Causation with Partial Causal Diagramscited by 1
  18. 2026General Probabilities of Causation with Causal Knowledgecited by 1
  19. 2024A Dynamical View of the Question of Whycited by 0
  20. 2024Causality in the Can: Diet Coke's Impact on Fatnesscited by 0
  21. 2025Identifying counterfactual probabilities using bivariate distributions and uplift modelingcited by 0
  22. 2026General sample size analysis for probabilities of causation: a delta method approachcited by 0
  23. 2026Causal Transfer in Medical Image Analysiscited by 0
  24. 2026Path-specific harm decomposition: A partial identification frameworkcited by 0
  25. 2026Same Predictions, Different Harms: Causal Auditing of Patient World Modelscited by 0