mlsanityData to 7 Oct 2026

Probabilities of Causation: Adequate Size of Experimental and Observational Samples

arXiv 2210.05027 ↗First posted 10 Oct 2022cs.AI cs.DMcited by 8

Not on the map. Why?

Abstract (arXiv)

The probabilities of causation are commonly used to solve decision-making problems. Tian and Pearl derived sharp bounds for the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of necessity (PN) using experimental and observational data. The assumption is that one is in possession of a large enough sample to permit an accurate estimation of the experimental and observational distributions. In this study, we present a method for determining the sample size needed for such estimation, when a given confidence interval (CI) is specified. We further show by simulation that the proposed sample size delivered stable estimations of the bounds of PNS.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 10 Oct 2022.

Builds on

7 of 17 references.

References · 7

  1. 2013Probabilities of Causation: Bounds and Identificationcited by 89
  2. 2021Causes of Effects: Learning individual responses from population datacited by 36
  3. 2022Personalized Decision Making -- A Conceptual Introductioncited by 29
  4. 2022Probabilities of Causation with Nonbinary Treatment and Effectcited by 24
  5. 2021Bounds on Causal Effects and Application to High Dimensional Datacited by 22
  6. 2021Unit Selection with Causal Diagramcited by 17
  7. 2022Unit Selection with Nonbinary Treatment and Effectcited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 2; 2026: 2.

8 papers.

  1. 2022Unit Selection: Learning Benefit Function from Finite Population Datacited by 6
  2. 2022Probabilities of Causation: Role of Observational Datacited by 5
  3. 2023Epsilon-Identifiability of Causal Quantitiescited by 4
  4. 2025Estimating Probabilities of Causation with Machine Learning Modelscited by 2
  5. 2025Learning Probabilities of Causation with Mask-Augmented Datacited by 1
  6. 2024Causality in the Can: Diet Coke's Impact on Fatnesscited by 0
  7. 2026General sample size analysis for probabilities of causation: a delta method approachcited by 0
  8. 2026Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networkscited by 0