mlsanityData to 14 Oct 2026

Unit Selection: Case Study and Comparison with A/B Test Heuristic

arXiv 2210.05030 ↗First posted 10 Oct 2022cs.AI cs.DMcited by 5

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Abstract (arXiv)

The unit selection problem defined by Li and Pearl identifies individuals who have desired counterfactual behavior patterns, for example, individuals who would respond positively if encouraged and would not otherwise. Li and Pearl showed by example that their unit selection model is beyond the A/B test heuristics. In this paper, we reveal the essence of the A/B test heuristics, which are exceptional cases of the benefit function defined by Li and Pearl. Furthermore, We provided more simulated use cases of Li-Pearl's unit selection model to help decision-makers apply their model correctly, explaining that A/B test heuristics are generally problematic.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 10 Oct 2022.

Builds on

7 of 23 references.

References · 7

  1. 2013Probabilities of Causation: Bounds and Identificationcited by 90
  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. 2022Unit Selection with Nonbinary Treatment and Effectcited by 12
  7. 2014Counterfactual Estimation and Optimization of Click Metrics for Search Enginescited by 5

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 1.

5 papers.