The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation

• URL: http://arxiv.org/abs/2309.17283v2
• Date: Sat, 14 Oct 2023 04:11:24 GMT
• Title: The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation
• Authors: Yong Wu, Mingzhou Liu, Jing Yan, Yanwei Fu, Shouyan Wang, Yizhou Wang, Xinwei Sun
• Abstract summary: Existing studies leveraged proxy variables or multiple treatments to adjust for confounding bias. In many real-world scenarios, there is greater interest in studying the effects on multiple outcomes. We show that parallel studies of multiple outcomes involved in this setting can assist each other in causal identification.
• Score: 53.81860494566915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Assessing causal effects in the presence of unobserved confounding is a challenging problem. Existing studies leveraged proxy variables or multiple treatments to adjust for the confounding bias. In particular, the latter approach attributes the impact on a single outcome to multiple treatments, allowing estimating latent variables for confounding control. Nevertheless, these methods primarily focus on a single outcome, whereas in many real-world scenarios, there is greater interest in studying the effects on multiple outcomes. Besides, these outcomes are often coupled with multiple treatments. Examples include the intensive care unit (ICU), where health providers evaluate the effectiveness of therapies on multiple health indicators. To accommodate these scenarios, we consider a new setting dubbed as multiple treatments and multiple outcomes. We then show that parallel studies of multiple outcomes involved in this setting can assist each other in causal identification, in the sense that we can exploit other treatments and outcomes as proxies for each treatment effect under study. We proceed with a causal discovery method that can effectively identify such proxies for causal estimation. The utility of our method is demonstrated in synthetic data and sepsis disease.

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)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effects [0.1398098625978622]
  Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment. This paper discusses several non-parametric approaches for estimating heterogeneous treatment effects using data from multiple trials.
  arXiv  Detail & Related papers  (2023-03-28T20:43:00Z)
• TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective [50.675845725806724]
  We propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt) TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions. The proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-12-17T15:01:05Z)
• Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects [41.06974193338288]
  We consider data episodes among treatment groups in counterfactual inference as meta-learning tasks. We train a meta-learner from a set of source treatment groups with sufficient samples and update the model by gradient descent with limited samples in target treatment. We perform experiments on two real-world datasets to evaluate inference accuracy and generalization ability.
  arXiv  Detail & Related papers  (2022-08-13T23:22:12Z)
• 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)
• On Inductive Biases for Heterogeneous Treatment Effect Estimation [91.3755431537592]
  We investigate how to exploit structural similarities of an individual's potential outcomes (POs) under different treatments. We compare three end-to-end learning strategies to overcome this problem.
  arXiv  Detail & Related papers  (2021-06-07T16:30:46Z)
• Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes [0.0]
  Causal inference with multiple treatments and rare outcomes is a subject that has been treated sparingly in the literature. This paper designs three sets of simulations, representative of the structure of our healthcare database study, and propose causal analysis strategies for such settings.
  arXiv  Detail & Related papers  (2020-08-18T01:26:36Z)
• 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.