# Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters

arXiv: https://arxiv.org/abs/2104.04103 · HTML: https://mlsanity.com/paper/2104.04103/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 71 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 18 of 18)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition](https://mlsanity.com/paper/1707.02641/) (2017)
- [Removing Hidden Confounding by Experimental Grounding](https://mlsanity.com/paper/1810.11646/) (2018)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Decision Trees for Decision-Making under the Predict-then-Optimize Framework](https://mlsanity.com/paper/2003.00360/) (2020)
- [The State of Applied Econometrics - Causality and Policy Evaluation](https://mlsanity.com/paper/1607.00699/) (2016)
- [A comparison of methods for model selection when estimating individual treatment effects](https://mlsanity.com/paper/1804.05146/) (2018)
- [Combining Observational and Experimental Datasets Using Shrinkage Estimators](https://mlsanity.com/paper/2002.06708/) (2020)
- [Using Experiments to Correct for Selection in Observational Studies](https://mlsanity.com/paper/2006.09676/) (2020)
- [Targeting for long-term outcomes](https://mlsanity.com/paper/2010.15835/) (2020)
- [Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index](https://mlsanity.com/paper/1603.09326/) (2016)
- [A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation](https://mlsanity.com/paper/2004.11532/) (2020)
- [Combining observational and experimental data to find heterogeneous treatment effects](https://mlsanity.com/paper/1611.02385/) (2016)
- [Test & Roll: Profit-Maximizing A/B Tests](https://mlsanity.com/paper/1811.00457/) (2018)
- [Combining Observational and Experimental Data to Improve Efficiency Using Imperfect Instruments](https://mlsanity.com/paper/2010.05117/) (2020)

## Built on by (most cited first, 25 of 25)

- [A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling](https://mlsanity.com/paper/2111.10106/) (2021)
- [Procedural Memory Is Not All You Need: Bridging Cognitive Gaps in LLM-Based Agents](https://mlsanity.com/paper/2505.03434/) (2025)
- [Treatment Effect Estimation for Optimal Decision-Making](https://mlsanity.com/paper/2505.13092/) (2025)
- [Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions](https://mlsanity.com/paper/2203.02605/) (2022)
- [RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation](https://mlsanity.com/paper/2312.14095/) (2023)
- [The Value of Prediction in Identifying the Worst-Off](https://mlsanity.com/paper/2501.19334/) (2025)
- [Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression](https://mlsanity.com/paper/2207.09139/) (2022)
- [Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management](https://mlsanity.com/paper/2502.18321/) (2025)
- [Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment](https://mlsanity.com/paper/2211.07264/) (2022)
- [Uplift vs. predictive modeling: a theoretical analysis](https://mlsanity.com/paper/2309.12036/) (2023)
- [Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis](https://mlsanity.com/paper/2606.02671/) (2026)
- [Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization](https://mlsanity.com/paper/2206.12532/) (2022)
- [A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling](https://mlsanity.com/paper/2312.07206/) (2023)
- [Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?](https://mlsanity.com/paper/2406.11308/) (2024)
- [Observationally Informed Adaptive Causal Experimental Design](https://mlsanity.com/paper/2603.03785/) (2026)
- [Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference](https://mlsanity.com/paper/2606.03332/) (2026)
- [Meta-analysis of individualized treatment rules via sign-coherency](https://mlsanity.com/paper/2211.15476/) (2022)
- [Towards a Transportable Causal Network Model Based on Observational Healthcare Data](https://mlsanity.com/paper/2311.08427/) (2023)
- [PresAIse, A Prescriptive AI Solution for Enterprises](https://mlsanity.com/paper/2402.02006/) (2024)
- [Uncertainty-aware Causal Decision Making via Effect Bound Decomposition](https://mlsanity.com/paper/2601.22736/) (2026)
- [Decision-Aware Proximal Bridge Learning for Optimal Treatment Selection](https://mlsanity.com/paper/2605.16989/) (2026)
- [Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data](https://mlsanity.com/paper/2607.14318/) (2026)
- [AI Strategy: How to Choose What AI Product to Implement](https://mlsanity.com/paper/2607.23733/) (2026)
- [Detecting an Effect Is Not Learning to Act on It: A Reward-SNR Floor for LLM Acquisition Agents](https://mlsanity.com/paper/2608.10441/) (2026)
- [Should I stay or should I show? Learning to selectively disclose information](https://mlsanity.com/paper/2609.39818/) (2026)

## Abstract (arXiv)

Causal decision making (CDM) based on machine learning has become a routine part of business. Businesses algorithmically target offers, incentives, and recommendations to affect consumer behavior. Recently, we have seen an acceleration of research related to CDM and causal effect estimation (CEE) using machine-learned models. This article highlights an important perspective: CDM is not the same as CEE, and counterintuitively, accurate CEE is not necessary for accurate CDM. Our experience is that this is not well understood by practitioners or most researchers. Technically, the estimand of interest is different, and this has important implications both for modeling and for the use of statistical models for CDM. We draw on prior research to highlight three implications. (1) We should consider carefully the objective function of the causal machine learning, and if possible, optimize for accurate treatment assignment rather than for accurate effect-size estimation. (2) Confounding does not have the same effect on CDM as it does on CEE. The upshot is that for supporting CDM it may be just as good or even better to learn with confounded data as with unconfounded data. Finally, (3) causal statistical modeling may not be necessary to support CDM because a proxy target for statistical modeling might do as well or better. This third observation helps to explain at least one broad common CDM practice that seems wrong at first blush: the widespread use of non-causal models for targeting interventions. The last two implications are particularly important in practice, as acquiring (unconfounded) data on all counterfactuals can be costly and often impracticable. These observations open substantial research ground. We hope to facilitate research in this area by pointing to related articles from multiple contributing fields, including two dozen articles published the last three to four years.