Estimating Individual Treatment Effects with Time-Varying Confounders

• URL: http://arxiv.org/abs/2008.13620v2
• Date: Tue, 15 Dec 2020 16:34:59 GMT
• Title: Estimating Individual Treatment Effects with Time-Varying Confounders
• Authors: Ruoqi Liu, Changchang Yin, Ping Zhang
• Abstract summary: Estimating individual treatment effect (ITE) from observational data is meaningful and practical in healthcare. Existing work mainly relies on the strong ignorability assumption that no hidden confounders exist. We propose Deep Sequential Weighting (DSW) for estimating ITE with time-varying confounders.
• Score: 9.784193264717098
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating the individual treatment effect (ITE) from observational data is meaningful and practical in healthcare. Existing work mainly relies on the strong ignorability assumption that no hidden confounders exist, which may lead to bias in estimating causal effects. Some studies consider the hidden confounders are designed for static environment and not easily adaptable to a dynamic setting. In fact, most observational data (e.g., electronic medical records) is naturally dynamic and consists of sequential information. In this paper, we propose Deep Sequential Weighting (DSW) for estimating ITE with time-varying confounders. Specifically, DSW infers the hidden confounders by incorporating the current treatment assignments and historical information using a deep recurrent weighting neural network. The learned representations of hidden confounders combined with current observed data are leveraged for potential outcome and treatment predictions. We compute the time-varying inverse probabilities of treatment for re-weighting the population. We conduct comprehensive comparison experiments on fully-synthetic, semi-synthetic and real-world datasets to evaluate the performance of our model and baselines. Results demonstrate that our model can generate unbiased and accurate treatment effect by conditioning both time-varying observed and hidden confounders, paving the way for personalized medicine.

Related papers

• Estimating Individual Dose-Response Curves under Unobserved Confounders from Observational Data [6.166869525631879]
  We present ContiVAE, a novel framework for estimating causal effects of continuous treatments, measured by individual dose-response curves. We show that ContiVAE outperforms existing methods by up to 62%, demonstrating its robustness and flexibility.
  arXiv  Detail & Related papers  (2024-10-21T07:24:26Z)
• Inverse Probability of Treatment Weighting with Deep Sequence Models Enables Accurate treatment effect Estimation from Electronic Health Records [1.1824562114990471]
  Inverse probability of treatment weighting (IPTW) is a widely used propensity score method. We propose to utilize IPTW to estimate treatment effect in the presence of time-dependent confounding using claims records. Deep sequence models have demonstrated good performance in modeling EHRs for various downstream tasks.
  arXiv  Detail & Related papers  (2024-06-13T06:29:16Z)
• COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning [35.119957381211236]
  We introduce Counterfactual Self-Supervised Transformer (COSTAR), a novel approach that integrates self-supervised learning for improved historical representations. COSTAR yields superior performance in estimation accuracy and generalization to out-of-distribution data compared to existing models.
  arXiv  Detail & Related papers  (2023-11-01T22:38:14Z)
• Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Data [8.347630187110004]
  Existing approaches for estimating treatment effects from longitudinal observational data are usually built upon a strong assumption of "unconfoundedness" We propose the Variational Temporal Deconfounder (VTD), an approach that leverages deep variational embeddings in the longitudinal setting using proxies. We test our VTD method on both synthetic and real-world clinical data, and the results show that our approach is effective when hidden confounding is the leading bias compared to other existing models.
  arXiv  Detail & Related papers  (2022-07-23T16:43:12Z)
• 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)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data [68.29870617697532]
  We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) for time-varying confounding in longitudinal data. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data.
  arXiv  Detail & Related papers  (2020-10-28T15:05:08Z)
• Provably Efficient Causal Reinforcement Learning with Confounded Observational Data [135.64775986546505]
  We study how to incorporate the dataset (observational data) collected offline, which is often abundantly available in practice, to improve the sample efficiency in the online setting. We propose the deconfounded optimistic value iteration (DOVI) algorithm, which incorporates the confounded observational data in a provably efficient manner.
  arXiv  Detail & Related papers  (2020-06-22T14:49:33Z)
• 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)
• Learning Decomposed Representation for Counterfactual Inference [53.36586760485262]
  The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. We propose a synergistic learning framework to 1) identify confounders by learning representations of both confounders and non-confounders, 2) balance confounder with sample re-weighting technique, and simultaneously 3) estimate the treatment effect in observational studies via counterfactual inference.
  arXiv  Detail & Related papers  (2020-06-12T09:50:42Z)

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.