Robust Causal Learning for the Estimation of Average Treatment Effects

• URL: http://arxiv.org/abs/2209.01805v1
• Date: Mon, 5 Sep 2022 07:35:58 GMT
• Title: Robust Causal Learning for the Estimation of Average Treatment Effects
• Authors: Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu, Shumin Ma, Zhiri Yuan, Dongdong Wang, Zhixiang Huang
• Abstract summary: We propose a Robust Causal Learning (RCL) method to offset the deficiencies of the Double/Debiased Machine Learning (DML) estimators. Empirically, the comprehensive experiments show that i) the RCL estimators give more stable estimations of the causal parameters than the DML estimators.
• Score: 14.96459402684986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate ATE in the observational study. However, the DML estimators can suffer an error-compounding issue and even give an extreme estimate when the propensity scores are misspecified or very close to 0 or 1. Previous studies have overcome this issue through some empirical tricks such as propensity score trimming, yet none of the existing literature solves this problem from a theoretical standpoint. In this paper, we propose a Robust Causal Learning (RCL) method to offset the deficiencies of the DML estimators. Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue. Empirically, the comprehensive experiments show that i) the RCL estimators give more stable estimations of the causal parameters than the DML estimators, and ii) the RCL estimators outperform the traditional estimators and their variants when applying different machine learning models on both simulation and benchmark datasets.

Related papers

• Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning [53.25336975467293]
  We present the first theoretical error decomposition analysis of methods such as perplexity and self-consistency. Our analysis reveals a fundamental trade-off: perplexity methods suffer from substantial model error due to the absence of a proper consistency function. We propose Reasoning-Pruning Perplexity Consistency (RPC), which integrates perplexity with self-consistency, and Reasoning Pruning, which eliminates low-probability reasoning paths.
  arXiv  Detail & Related papers  (2025-02-01T18:09:49Z)
• Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data [0.9087641068861047]
  We discuss the impact that missing outcome data has on causal machine learning estimators for the conditional average treatment effect (CATE) We propose two de-biased machine learning estimators for the CATE, the mDR-learner and mEP-learner, which address the issue of under-representation.
  arXiv  Detail & Related papers  (2024-12-27T16:10:03Z)
• Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration [0.0]
  This paper investigates the use of probability calibration approaches within the Double/debiased machine learning framework. We show that calibrating propensity scores may significantly reduce the root mean squared error of DML estimates. We showcase it in an empirical example and provide conditions under which calibration does not alter the properties of the DML estimator.
  arXiv  Detail & Related papers  (2024-09-07T17:44:01Z)
• Estimating Causal Effects with Double Machine Learning -- A Method Evaluation [5.904095466127043]
  We review one of the most prominent methods - "double/debiased machine learning" (DML) Our findings indicate that the application of a suitably flexible machine learning algorithm within DML improves the adjustment for various nonlinear confounding relationships. When estimating the effects of air pollution on housing prices, we find that DML estimates are consistently larger than estimates of less flexible methods.
  arXiv  Detail & Related papers  (2024-03-21T13:21:33Z)
• Calibrating doubly-robust estimators with unbalanced treatment assignment [0.0]
  We propose a simple extension of the DML estimator which undersamples data for propensity score modeling. The paper provides theoretical results showing that the estimator retains the estimator's properties and calibrates scores to match the original distribution.
  arXiv  Detail & Related papers  (2024-03-03T18:40:11Z)
• Hyperparameter Tuning and Model Evaluation in Causal Effect Estimation [2.7823528791601686]
  This paper investigates the interplay between the four different aspects of model evaluation for causal effect estimation. We find that most causal estimators are roughly equivalent in performance if tuned thoroughly enough. We call for more research into causal model evaluation to unlock the optimum performance not currently being delivered even by state-of-the-art procedures.
  arXiv  Detail & Related papers  (2023-03-02T17:03:02Z)
• Learning to Estimate Without Bias [57.82628598276623]
  Gauss theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non linear settings via deep learning with bias constraints. A second motivation to BCE is in applications where multiple estimates of the same unknown are averaged for improved performance.
  arXiv  Detail & Related papers  (2021-10-24T10:23:51Z)
• Counterfactual Maximum Likelihood Estimation for Training Deep Networks [83.44219640437657]
  Deep learning models are prone to learning spurious correlations that should not be learned as predictive clues. We propose a causality-based training framework to reduce the spurious correlations caused by observable confounders. We conduct experiments on two real-world tasks: Natural Language Inference (NLI) and Image Captioning.
  arXiv  Detail & Related papers  (2021-06-07T17:47:16Z)
• Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model [71.9860741092209]
  Clinical researchers often select among and evaluate risk prediction models. Standard metrics calculated from retrospective data are only related to model utility under certain assumptions. When predictions are delivered repeatedly throughout time, the relationship between standard metrics and utility is further complicated.
  arXiv  Detail & Related papers  (2020-06-02T16:26:49Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)
• Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond [69.83813153444115]
  We consider an efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference. Debiased machine learning (DML) is a data-splitting approach to estimating high-dimensional nuisances. We propose localized debiased machine learning (LDML), which avoids this burdensome step.
  arXiv  Detail & Related papers  (2019-12-30T14:42:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.