# Personalized Decision Making -- A Conceptual Introduction

arXiv: https://arxiv.org/abs/2208.09558 · HTML: https://mlsanity.com/paper/2208.09558/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 2 of 2)

- [Probabilities of Causation: Bounds and Identification](https://mlsanity.com/paper/1301.3898/) (2013)
- [Causes of Effects: Learning individual responses from population data](https://mlsanity.com/paper/2104.13730/) (2021)

## Built on by (most cited first, 30 of 30)

- [End-To-End Causal Effect Estimation from Unstructured Natural Language Data](https://mlsanity.com/paper/2407.07018/) (2024)
- [Quantifying Harm](https://mlsanity.com/paper/2209.15111/) (2022)
- [Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources](https://mlsanity.com/paper/2307.16577/) (2023)
- [Probabilities of Causation: Adequate Size of Experimental and Observational Samples](https://mlsanity.com/paper/2210.05027/) (2022)
- [Causal Contrastive Learning for Counterfactual Regression Over Time](https://mlsanity.com/paper/2406.00535/) (2024)
- [Probabilities of Causation: Role of Observational Data](https://mlsanity.com/paper/2210.08874/) (2022)
- [Epsilon-Identifiability of Causal Quantities](https://mlsanity.com/paper/2301.12022/) (2023)
- [Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment](https://mlsanity.com/paper/2211.07264/) (2022)
- [Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxies](https://mlsanity.com/paper/2303.05396/) (2023)
- [Bounding probabilities of causation through the causal marginal problem](https://mlsanity.com/paper/2304.02023/) (2023)
- [The Limits of Predicting Agents from Behaviour](https://mlsanity.com/paper/2506.02923/) (2025)
- [Nondeterministic Causal Models](https://mlsanity.com/paper/2405.14001/) (2024)
- [Statistical Decision Theory with Counterfactual Loss](https://mlsanity.com/paper/2505.08908/) (2025)
- [Tightening Bounds on Probabilities of Causation By Merging Datasets](https://mlsanity.com/paper/2310.08406/) (2023)
- [Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data](https://mlsanity.com/paper/2310.10559/) (2023)
- [Distribution-consistency Structural Causal Models](https://mlsanity.com/paper/2401.15911/) (2024)
- [Identification of Probabilities of Causation: from Recursive to Closed-Form Bounds](https://mlsanity.com/paper/2505.15274/) (2025)
- [Causal Counterfactuals Reconsidered](https://mlsanity.com/paper/2512.12804/) (2025)
- [Preference-based Conditional Treatment Effects and Policy Learning](https://mlsanity.com/paper/2602.03823/) (2026)
- [Bounding Probabilities of Causation with Partial Causal Diagrams](https://mlsanity.com/paper/2602.14503/) (2026)
- [Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data](https://mlsanity.com/paper/2212.02932/) (2022)
- [Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI](https://mlsanity.com/paper/2308.14474/) (2023)
- [Answering Causal Queries at Layer 3 with DiscoSCMs-Embracing Heterogeneity](https://mlsanity.com/paper/2309.09323/) (2023)
- [Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners](https://mlsanity.com/paper/2504.15386/) (2025)
- [What is a good matching of probability measures? A counterfactual lens on transport maps](https://mlsanity.com/paper/2509.16027/) (2025)
- [General sample size analysis for probabilities of causation: a delta method approach](https://mlsanity.com/paper/2602.17070/) (2026)
- [Optimal Experiments for Partial Causal Effect Identification](https://mlsanity.com/paper/2605.06993/) (2026)
- [Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks](https://mlsanity.com/paper/2605.07065/) (2026)
- [Counterfactually Safe Reinforcement Learning](https://mlsanity.com/paper/2605.25114/) (2026)
- [CoBe: A Benchmark For Conversational Counterfactual Text Editing](https://mlsanity.com/paper/2610.13057/) (2026)

## 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.