# Double/Debiased Machine Learning for Treatment and Causal Parameters

arXiv: https://arxiv.org/abs/1608.00060 · HTML: https://mlsanity.com/paper/1608.00060/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2016.

## Built on by (most cited first, 30 of 43)

- [CausalPFN: Amortized Causal Effect Estimation via In-Context Learning](https://mlsanity.com/paper/2506.07918/) (2025)
- [Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits](https://mlsanity.com/paper/2106.02029/) (2021)
- [Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation](https://mlsanity.com/paper/2211.01939/) (2022)
- [Causal Estimation for Text Data with (Apparent) Overlap Violations](https://mlsanity.com/paper/2210.00079/) (2022)
- [Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach](https://mlsanity.com/paper/2310.06680/) (2023)
- [Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data](https://mlsanity.com/paper/2210.08642/) (2022)
- [Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding](https://mlsanity.com/paper/2302.10625/) (2023)
- [Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions](https://mlsanity.com/paper/2207.04771/) (2022)
- [Partial Identification of Dose Responses with Hidden Confounders](https://mlsanity.com/paper/2204.11206/) (2022)
- [Learning Complementary Policies for Human-AI Teams](https://mlsanity.com/paper/2302.02944/) (2023)
- [Causality-Aided Trade-off Analysis for Machine Learning Fairness](https://mlsanity.com/paper/2305.13057/) (2023)
- [Estimation Beyond Data Reweighting: Kernel Method of Moments](https://mlsanity.com/paper/2305.10898/) (2023)
- [Imputation-Powered Inference](https://mlsanity.com/paper/2509.13778/) (2025)
- [Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark](https://mlsanity.com/paper/2109.12769/) (2021)
- [Variable Importance Matching for Causal Inference](https://mlsanity.com/paper/2302.11715/) (2023)
- [Differentiable Cyclic Causal Discovery Under Unmeasured Confounders](https://mlsanity.com/paper/2508.08450/) (2025)
- [Individual Treatment Effect Estimation Through Controlled Neural Network Training in Two Stages](https://mlsanity.com/paper/2201.08559/) (2022)
- [OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators](https://mlsanity.com/paper/2405.17708/) (2024)
- [Pre-Trained AI Model Assisted Online Decision-Making under Missing Covariates: A Theoretical Perspective](https://mlsanity.com/paper/2507.07852/) (2025)
- [The Necessity of Setting Temperature in LLM-as-a-Judge](https://mlsanity.com/paper/2603.28304/) (2026)
- [CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies](https://mlsanity.com/paper/2408.12004/) (2024)
- [DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification](https://mlsanity.com/paper/2410.20295/) (2024)
- [DeepBlip: Estimating Conditional Average Treatment Effects Over Time](https://mlsanity.com/paper/2511.14545/) (2025)
- [GBOSE: Generalized Bandit Orthogonalized Semiparametric Estimation](https://mlsanity.com/paper/2301.08781/) (2023)
- [Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data](https://mlsanity.com/paper/2310.10559/) (2023)
- [A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators](https://mlsanity.com/paper/2510.16419/) (2025)
- [When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels](https://mlsanity.com/paper/2609.13148/) (2026)
- [Linking a predictive model to causal effect estimation](https://mlsanity.com/paper/2304.04566/) (2023)
- [Heterogeneous Treatment Effects in Panel Data](https://mlsanity.com/paper/2406.05633/) (2024)
- [Generative Learner for Distributional Causal Effects](https://mlsanity.com/paper/2503.17894/) (2025)

## Abstract (arXiv)

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual regression coefficients, average treatment effects, average lifts, and demand or supply elasticities. In fact, estimates of such causal parameters obtained via naively plugging ML estimators into estimating equations for such parameters can behave very poorly due to the regularization bias. Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools. Specifically, we can form an orthogonal score for the target low-dimensional parameter by combining auxiliary and main ML predictions. The score is then used to build a de-biased estimator of the target parameter which typically will converge at the fastest possible 1/root(n) rate and be approximately unbiased and normal, and from which valid confidence intervals for these parameters of interest may be constructed. The resulting method thus could be called a "double ML" method because it relies on estimating primary and auxiliary predictive models. In order to avoid overfitting, our construction also makes use of the K-fold sample splitting, which we call cross-fitting. This allows us to use a very broad set of ML predictive methods in solving the auxiliary and main prediction problems, such as random forest, lasso, ridge, deep neural nets, boosted trees, as well as various hybrids and aggregators of these methods.