mlsanityData to 14 Oct 2026

Unit Selection with Causal Diagram

arXiv 2109.07556 ↗First posted 15 Sep 2021cs.AIcited by 17

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Abstract (arXiv)

The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if encouraged and a different way if not encouraged. Using a combination of experimental and observational data, Li and Pearl derived tight bounds on the "benefit function" - the payoff/cost associated with selecting an individual with given characteristics. This paper shows that these bounds can be narrowed significantly (enough to change decisions) when structural information is available in the form of a causal model. We address the problem of estimating the benefit function using observational and experimental data when specific graphical criteria are assumed to hold.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 3 of its 14 references that are in the map. First posted on arXiv 15 Sep 2021.

Builds on

3 of 14 references.

References · 3

  1. 2013Probabilities of Causation: Bounds and Identificationcited by 90
  2. 2021Causes of Effects: Learning individual responses from population datacited by 37
  3. 2014Counterfactual Estimation and Optimization of Click Metrics for Search Enginescited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 7; 2023: 4; 2024: 4; 2025: 1; 2026: 1.

17 papers.

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  1. 2022Probabilities of Causation with Nonbinary Treatment and Effectcited by 24
  2. 2022Unit Selection with Nonbinary Treatment and Effectcited by 12
  3. 2022Probabilities of Causation: Adequate Size of Experimental and Observational Samplescited by 8
  4. 2024Probabilities of Causation for Continuous and Vector Variablescited by 8
  5. 2022Unit Selection: Learning Benefit Function from Finite Population Datacited by 6
  6. 2022Unit Selection: Case Study and Comparison with A/B Test Heuristiccited by 5
  7. 2022Probabilities of Causation: Role of Observational Datacited by 5
  8. 2023Epsilon-Identifiability of Causal Quantitiescited by 4
  9. 2022Partial counterfactual identification and uplift modeling: theoretical results and real-world assessmentcited by 3
  10. 2023Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxiescited by 3
  11. 2024Unifying Invariance and Spuriousity for Graph Out-of-Distribution via Probability of Necessity and Sufficiencycited by 3
  12. 2024Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiencycited by 2
5 more
  1. 2023An Algorithm and Complexity Results for Causal Unit Selectioncited by 1
  2. 2024Distribution-consistency Structural Causal Modelscited by 1
  3. 2025Identification of Probabilities of Causation: from Recursive to Closed-Form Boundscited by 1
  4. 2023Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FIcited by 0
  5. 2026General sample size analysis for probabilities of causation: a delta method approachcited by 0