mlsanityData to 14 Oct 2026

Unit Selection with Nonbinary Treatment and Effect

arXiv 2208.09569 ↗First posted 20 Aug 2022cs.AIcited by 12

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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", which is the payoff/cost associated with selecting an individual with given characteristics. This paper extends the benefit function to the general form such that the treatment and effect are not restricted to binary. We propose an algorithm to test the identifiability of the nonbinary benefit function and an algorithm to compute the bounds of the nonbinary benefit function using experimental and observational data.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 20 Aug 2022.

Builds on

6 of 22 references.

References · 6

  1. 2021Partial Counterfactual Identification from Observational and Experimental Datacited by 64
  2. 2021Causes of Effects: Learning individual responses from population datacited by 37
  3. 2022Probabilities of Causation with Nonbinary Treatment and Effectcited by 24
  4. 2021Bounds on Causal Effects and Application to High Dimensional Datacited by 22
  5. 2021Unit Selection with Causal Diagramcited by 17
  6. 2014Counterfactual Estimation and Optimization of Click Metrics for Search Enginescited by 5

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Cited by year

Citing papers per year: 2022: 5; 2023: 2; 2024: 2; 2025: 3.

12 papers.

  1. 2022Probabilities of Causation: Adequate Size of Experimental and Observational Samplescited by 8
  2. 2024Probabilities of Causation for Continuous and Vector Variablescited by 8
  3. 2022On the Complexity of Counterfactual Reasoningcited by 7
  4. 2022Unit Selection: Learning Benefit Function from Finite Population Datacited by 6
  5. 2022Unit Selection: Case Study and Comparison with A/B Test Heuristiccited by 5
  6. 2022Probabilities of Causation: Role of Observational Datacited by 5
  7. 2023Epsilon-Identifiability of Causal Quantitiescited by 4
  8. 2024Causal Unit Selection using Tractable Arithmetic Circuitscited by 3
  9. 2025Estimating Probabilities of Causation with Machine Learning Modelscited by 2
  10. 2025Statistical Decision Theory with Counterfactual Losscited by 2
  11. 2023An Algorithm and Complexity Results for Causal Unit Selectioncited by 1
  12. 2025Learning Probabilities of Causation with Mask-Augmented Datacited by 1