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Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods

arXiv 1701.05306 ↗First posted 19 Jan 2017stat.MLcited by 21

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Abstract (arXiv)

Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find accurate estimation of individual treatment effects is possible even in complex heterogeneous settings but that the type of RF approach plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, in order to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jan 2017.

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Citing papers per year: 2021: 6; 2022: 7; 2023: 3; 2024: 2; 2025: 3.

21 papers.

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  1. 2024Causal Estimation of Memorisation Profilescited by 20
  2. 2025Causal Estimation of Tokenisation Biascited by 16
  3. 2021On the Distinction Between "Conditional Average Treatment Effects" (CATE) and "Individual Treatment Effects" (ITE) Under Ignorability Assumptionscited by 9
  4. 2021Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimationcited by 7
  5. 2023Doubly Robust Counterfactual Classificationcited by 5
  6. 2022Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performancecited by 4
  7. 2022What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?cited by 4
  8. 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncited by 4
  9. 2022Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forestscited by 3
  10. 2021CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Searchcited by 2
  11. 2023DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Datacited by 2
  12. 2021The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted L1 regularized Neural Networks Predictionscited by 1
9 more
  1. 2022BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effectcited by 1
  2. 2025Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networkscited by 1
  3. 2021Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptionscited by 0
  4. 2021Non parametric estimation of causal populations in a counterfactual scenariocited by 0
  5. 2022A Bayesian Semiparametric Method For Estimating Causal Quantile Effectscited by 0
  6. 2022TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspectivecited by 0
  7. 2023Linking a predictive model to causal effect estimationcited by 0
  8. 2024Are causal effect estimations enough for optimal recommendations under multitreatment scenarios?cited by 0
  9. 2025Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Treescited by 0