mlsanityData to 7 Oct 2026

Personalized Decision Making -- A Conceptual Introduction

arXiv 2208.09558 ↗First posted 19 Aug 2022cs.AI cs.LO stat.MEcited by 29

Not on the map. Why?

Abstract (arXiv)

Personalized decision making targets the behavior of a specific individual, while population-based decision making concerns a sub-population resembling that individual. This paper clarifies the distinction between the two and explains why the former leads to more informed decisions. We further show that by combining experimental and observational studies we can obtain valuable information about individual behavior and, consequently, improve decisions over those obtained from experimental studies alone.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 2 of its 24 references that are in the map. First posted on arXiv 19 Aug 2022.

Builds on

2 of 24 references.

References · 2

  1. 2013Probabilities of Causation: Bounds and Identificationcited by 89
  2. 2021Causes of Effects: Learning individual responses from population datacited by 36

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 8; 2024: 4; 2025: 6; 2026: 6.

29 papers.

Sort:

  1. 2024End-To-End Causal Effect Estimation from Unstructured Natural Language Datacited by 18
  2. 2022Quantifying Harmcited by 10
  3. 2023Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sourcescited by 9
  4. 2022Probabilities of Causation: Adequate Size of Experimental and Observational Samplescited by 8
  5. 2024Causal Contrastive Learning for Counterfactual Regression Over Timecited by 7
  6. 2022Probabilities of Causation: Role of Observational Datacited by 5
  7. 2023Epsilon-Identifiability of Causal Quantitiescited by 4
  8. 2022Partial counterfactual identification and uplift modeling: theoretical results and real-world assessmentcited by 3
  9. 2023Bounding probabilities of causation through the causal marginal problemcited by 3
  10. 2025The Limits of Predicting Agents from Behaviourcited by 3
  11. 2023Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxiescited by 2
  12. 2024Nondeterministic Causal Modelscited by 2
17 more
  1. 2025Statistical Decision Theory with Counterfactual Losscited by 2
  2. 2023Tightening Bounds on Probabilities of Causation By Merging Datasetscited by 1
  3. 2023Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Datacited by 1
  4. 2024Distribution-consistency Structural Causal Modelscited by 1
  5. 2025Identification of Probabilities of Causation: from Recursive to Closed-Form Boundscited by 1
  6. 2025Causal Counterfactuals Reconsideredcited by 1
  7. 2022Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Datacited by 0
  8. 2023Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FIcited by 0
  9. 2023Answering Causal Queries at Layer 3 with DiscoSCMs-Embracing Heterogeneitycited by 0
  10. 2025Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learnerscited by 0
  11. 2025What is a good matching of probability measures? A counterfactual lens on transport mapscited by 0
  12. 2026Preference-based Conditional Treatment Effects and Policy Learningcited by 0
  13. 2026Bounding Probabilities of Causation with Partial Causal Diagramscited by 0
  14. 2026General sample size analysis for probabilities of causation: a delta method approachcited by 0
  15. 2026Optimal Experiments for Partial Causal Effect Identificationcited by 0
  16. 2026Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networkscited by 0
  17. 2026Counterfactually Safe Reinforcement Learningcited by 0