Conformal Diffusion Models for Individual Treatment Effect Estimation and Inference

• URL: http://arxiv.org/abs/2408.01582v1
• Date: Fri, 2 Aug 2024 21:35:08 GMT
• Title: Conformal Diffusion Models for Individual Treatment Effect Estimation and Inference
• Authors: Hengrui Cai, Huaqing Jin, Lexin Li,
• Abstract summary: Individual treatment effect offers the most granular measure of treatment effect on an individual level. We propose a novel conformal diffusion model-based approach that addresses those intricate challenges.
• Score: 6.406853903837333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating treatment effects from observational data is of central interest across numerous application domains. Individual treatment effect offers the most granular measure of treatment effect on an individual level, and is the most useful to facilitate personalized care. However, its estimation and inference remain underdeveloped due to several challenges. In this article, we propose a novel conformal diffusion model-based approach that addresses those intricate challenges. We integrate the highly flexible diffusion modeling, the model-free statistical inference paradigm of conformal inference, along with propensity score and covariate local approximation that tackle distributional shifts. We unbiasedly estimate the distributions of potential outcomes for individual treatment effect, construct an informative confidence interval, and establish rigorous theoretical guarantees. We demonstrate the competitive performance of the proposed method over existing solutions through extensive numerical studies.

Related papers

• Conformal Counterfactual Inference under Hidden Confounding [19.190396053530417]
  Predicting potential outcomes along with its uncertainty in a counterfactual world poses the foundamental challenge in causal inference. Existing methods that construct confidence intervals for counterfactuals either rely on the assumption of strong ignorability. We propose a novel approach based on transductive weighted conformal prediction, which provides confidence intervals for counterfactual outcomes with marginal converage guarantees.
  arXiv  Detail & Related papers  (2024-05-20T21:43:43Z)
• Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation [137.3520153445413]
  A notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. We evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes.
  arXiv  Detail & Related papers  (2023-07-11T02:58:10Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Counterfactual Generative Models for Time-Varying Treatments [15.208067770012283]
  Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science. We propose a novel conditional generative framework capable of producing counterfactual samples under time-varying treatment. We present a thorough evaluation of our method using both synthetic and real-world data.
  arXiv  Detail & Related papers  (2023-05-25T05:45:53Z)
• Causal Inference under Data Restrictions [0.0]
  This dissertation focuses on modern causal inference under uncertainty and data restrictions. It includes applications to neoadjuvant clinical trials, distributed data networks, and robust individualized decision making.
  arXiv  Detail & Related papers  (2023-01-20T20:14:32Z)
• 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)
• Distributionally Robust Causal Inference with Observational Data [4.8986598953553555]
  We consider the estimation of average treatment effects in observational studies without the standard assumption of unconfoundedness. We propose a new framework of robust causal inference under the general observational study setting with the possible existence of unobserved confounders.
  arXiv  Detail & Related papers  (2022-10-15T16:02:33Z)
• 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)
• Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks [103.14809802212535]
  We build on the generative adversarial networks (GANs) framework to address the problem of estimating the effect of continuous-valued interventions. Our model, SCIGAN, is flexible and capable of simultaneously estimating counterfactual outcomes for several different continuous interventions. To address the challenges presented by shifting to continuous interventions, we propose a novel architecture for our discriminator.
  arXiv  Detail & Related papers  (2020-02-27T18:46:21Z)
• 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)
• Nonparametric inference for interventional effects with multiple mediators [0.0]
  We provide theory that allows for more flexible, possibly machine learning-based, estimation techniques. We demonstrate multiple robustness properties of the proposed estimators. Our work thus provides a means of leveraging modern statistical learning techniques in estimation of interventional mediation effects.
  arXiv  Detail & Related papers  (2020-01-16T19:05:00Z)

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.