Treatment Effect Estimation with Observational Network Data using Machine Learning

• URL: http://arxiv.org/abs/2206.14591v3
• Date: Mon, 4 Sep 2023 21:59:05 GMT
• Title: Treatment Effect Estimation with Observational Network Data using Machine Learning
• Authors: Corinne Emmenegger and Meta-Lina Spohn and Timon Elmer and Peter B\"uhlmann
• Abstract summary: Causal inference methods for treatment effect estimation usually assume independent units. We develop augmented inverse probability (AIPW) for estimation and inference of the direct effect of the treatment with observational data from a single (social) network with spillover effects.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in spillover effects between units. We develop augmented inverse probability weighting (AIPW) for estimation and inference of the direct effect of the treatment with observational data from a single (social) network with spillover effects. We use plugin machine learning and sample splitting to obtain a semiparametric treatment effect estimator that converges at the parametric rate and asymptotically follows a Gaussian distribution. We apply our AIPW method to the Swiss StudentLife Study data to investigate the effect of hours spent studying on exam performance accounting for the students' social network.

Related papers

• 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)
• Semiparametric inference for impulse response functions using double/debiased machine learning [49.1574468325115]
  We introduce a machine learning estimator for the impulse response function (IRF) in settings where a time series of interest is subjected to multiple discrete treatments. The proposed estimator can rely on fully nonparametric relations between treatment and outcome variables, opening up the possibility to use flexible machine learning approaches to estimate IRFs.
  arXiv  Detail & Related papers  (2024-11-15T07:42:02Z)
• 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)
• Causal Message Passing for Experiments with Unknown and General Network Interference [5.294604210205507]
  We introduce a new framework to accommodate complex and unknown network interference. Our framework, termed causal message-passing, is grounded in high-dimensional approximate message passing methodology. We demonstrate the effectiveness of this approach across five numerical scenarios.
  arXiv  Detail & Related papers  (2023-11-14T17:31:50Z)
• Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning [0.0]
  We suggest a machine learning estimator of direct and indirect quantile treatment effects under a selection-on-observables assumption. The proposed method is based on the efficient score functions of the cumulative distribution functions of potential outcomes. We also propose a multiplier bootstrap for statistical inference and show the validity of the multiplier.
  arXiv  Detail & Related papers  (2023-07-03T14:27:15Z)
• B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding [51.74479522965712]
  We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on hidden confounding. We prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods.
  arXiv  Detail & Related papers  (2023-04-20T18:07:19Z)
• Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time [71.30985926640659]
  We introduce the Disentangled Counterfactual Recurrent Network (DCRN), a sequence-to-sequence architecture that estimates treatment outcomes over time. With an architecture that is completely inspired by the causal structure of treatment influence over time, we advance forecast accuracy and disease understanding. We demonstrate that DCRN outperforms current state-of-the-art methods in forecasting treatment responses, on both real and simulated data.
  arXiv  Detail & Related papers  (2021-12-07T16:40:28Z)
• 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)
• Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
  Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
  arXiv  Detail & Related papers  (2020-06-14T01:15:00Z)
• Causal Inference under Networked Interference and Intervention Policy Enhancement [35.149125599812706]
  Estimating individual treatment effects from data of randomized experiments is a critical task in causal inference. Usually, in randomized experiments or observational studies with interconnected units, one can only observe treatment responses under interference. We study causal effect estimation under general network interference using GNNs, which are powerful tools for capturing the dependency in the graph.
  arXiv  Detail & Related papers  (2020-02-20T00:35:50Z)
• 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.