Data-Driven Estimation of Heterogeneous Treatment Effects

• URL: http://arxiv.org/abs/2301.06615v2
• Date: Thu, 17 Oct 2024 17:40:25 GMT
• Title: Data-Driven Estimation of Heterogeneous Treatment Effects
• Authors: Christopher Tran, Keith Burghardt, Kristina Lerman, Elena Zheleva,
• Abstract summary: Estimating how a treatment affects different individuals, known as heterogeneous treatment effect estimation, is an important problem in empirical sciences. We provide a survey of state-of-the-art data-driven methods for heterogeneous treatment effect estimation using machine learning.
• Score: 15.140272661540655
• License:
• Abstract: Estimating how a treatment affects different individuals, known as heterogeneous treatment effect estimation, is an important problem in empirical sciences. In the last few years, there has been a considerable interest in adapting machine learning algorithms to the problem of estimating heterogeneous effects from observational and experimental data. However, these algorithms often make strong assumptions about the observed features in the data and ignore the structure of the underlying causal model, which can lead to biased estimation. At the same time, the underlying causal mechanism is rarely known in real-world datasets, making it hard to take it into consideration. In this work, we provide a survey of state-of-the-art data-driven methods for heterogeneous treatment effect estimation using machine learning, broadly categorizing them as methods that focus on counterfactual prediction and methods that directly estimate the causal effect. We also provide an overview of a third category of methods which rely on structural causal models and learn the model structure from data. Our empirical evaluation under various underlying structural model mechanisms shows the advantages and deficiencies of existing estimators and of the metrics for measuring their performance.

Related papers

• Higher-Order Causal Message Passing for Experimentation with Complex Interference [6.092214762701847]
  We introduce a new class of estimators based on causal message-passing, specifically designed for settings with pervasive, unknown interference. Our estimator draws on information from the sample mean and variance of unit outcomes and treatments over time, enabling efficient use of observed data.
  arXiv  Detail & Related papers  (2024-11-01T18:00:51Z)
• An evaluation framework for comparing causal inference models [3.1372269816123994]
  We use the proposed evaluation methodology to compare several state-of-the-art causal effect estimation models. The main motivation behind this approach is the elimination of the influence of a small number of instances or simulation on the benchmarking process.
  arXiv  Detail & Related papers  (2022-08-31T21:04:20Z)
• Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty [13.452510519858995]
  Estimating how a treatment affects units individually, known as heterogeneous treatment effect (HTE) estimation, is an essential part of decision-making and policy implementation. We develop a feature selection method that considers each feature's value for HTE estimation and learns the relevant parts of the causal structure from data.
  arXiv  Detail & Related papers  (2022-06-25T16:26:35Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators [91.3755431537592]
  We show that even in arguably the simplest setting, estimation under ignorability assumptions can be misleading. We consider two popular machine learning benchmark datasets for evaluation of heterogeneous treatment effect estimators. We highlight that the inherent characteristics of the benchmark datasets favor some algorithms over others.
  arXiv  Detail & Related papers  (2021-07-28T13:21:27Z)
• Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions [68.6505592770171]
  Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
  arXiv  Detail & Related papers  (2021-03-08T14:29:07Z)
• Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review [0.0]
  We introduce the setup and the issues related to conducting causal inference with observational or non-fully randomized data. We develop a unifying taxonomy of the existing state-of-the-art frameworks that allow for individual treatment effects estimation. We conclude by demonstrating the use of some of the methods on an empirical analysis of the school meal program data.
  arXiv  Detail & Related papers  (2020-09-14T14:26:55Z)
• Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects [61.03579766573421]
  We study estimation of individual-level causal effects, such as a single patient's response to alternative medication. We devise representation learning algorithms that minimize our bound, by regularizing the representation's induced treatment group distance. We extend these algorithms to simultaneously learn a weighted representation to further reduce treatment group distances.
  arXiv  Detail & Related papers  (2020-01-21T10:16:33Z)

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.