Partial Identification of Dose Responses with Hidden Confounders

• URL: http://arxiv.org/abs/2204.11206v3
• Date: Mon, 12 Jun 2023 20:59:08 GMT
• Title: Partial Identification of Dose Responses with Hidden Confounders
• Authors: Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg
• Abstract summary: Inferring causal effects of continuous-valued treatments from observational data is a crucial task. We present novel methodology to bound both average and conditional average continuous-valued treatment-effect estimates. We apply our method to a real-world observational case study to demonstrate the value of identifying dose-dependent causal effects.
• Score: 25.468473751289036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inferring causal effects of continuous-valued treatments from observational data is a crucial task promising to better inform policy- and decision-makers. A critical assumption needed to identify these effects is that all confounding variables -- causal parents of both the treatment and the outcome -- are included as covariates. Unfortunately, given observational data alone, we cannot know with certainty that this criterion is satisfied. Sensitivity analyses provide principled ways to give bounds on causal estimates when confounding variables are hidden. While much attention is focused on sensitivity analyses for discrete-valued treatments, much less is paid to continuous-valued treatments. We present novel methodology to bound both average and conditional average continuous-valued treatment-effect estimates when they cannot be point identified due to hidden confounding. A semi-synthetic benchmark on multiple datasets shows our method giving tighter coverage of the true dose-response curve than a recently proposed continuous sensitivity model and baselines. Finally, we apply our method to a real-world observational case study to demonstrate the value of identifying dose-dependent causal effects.

Related papers

• Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner [72.20769640318969]
  Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments. Medical practitioners require not only estimating averaged causal quantities, but also understanding the randomness of the treatment effect as a random variable. This randomness is referred to as aleatoric uncertainty and is necessary for understanding the probability of benefit from treatment or quantiles of the treatment effect.
  arXiv  Detail & Related papers  (2024-11-05T18:14:49Z)
• 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)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Sharp Bounds for Generalized Causal Sensitivity Analysis [30.77874108094485]
  We propose a unified framework for causal sensitivity analysis under unobserved confounding. This includes (conditional) average treatment effects, effects for mediation analysis and path analysis, and distributional effects. Our bounds for (conditional) average treatment effects coincide with recent optimality results for causal sensitivity analysis.
  arXiv  Detail & Related papers  (2023-05-26T14:44:32Z)
• 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)
• Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions [34.19821413853115]
  Estimating the effects of continuous-valued interventions from observational data is critically important in fields such as climate science, healthcare, and economics. We develop a continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds that agree with both the observed data and a researcher-defined level of hidden confounding. We introduce a scalable algorithm to derive the bounds and uncertainty-aware deep models to efficiently estimate these bounds for high-dimensional, large-sample observational data.
  arXiv  Detail & Related papers  (2022-04-21T11:15:10Z)
• Assessment of Treatment Effect Estimators for Heavy-Tailed Data [70.72363097550483]
  A central obstacle in the objective assessment of treatment effect (TE) estimators in randomized control trials (RCTs) is the lack of ground truth (or validation set) to test their performance. We provide a novel cross-validation-like methodology to address this challenge. We evaluate our methodology across 709 RCTs implemented in the Amazon supply chain.
  arXiv  Detail & Related papers  (2021-12-14T17:53:01Z)
• 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)
• Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions [15.443178111068418]
  We propose a split-treatment analysis that ranks the individuals most likely to be positively affected by a prospective intervention. We show that the ranking of heterogeneous causal effect based on the proxy treatment is the same as the ranking based on the target treatment's effect.
  arXiv  Detail & Related papers  (2020-11-11T16:17:29Z)
• 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)
• Conformal Inference of Counterfactuals and Individual Treatment Effects [6.810856082577402]
  We propose a conformal inference-based approach that can produce reliable interval estimates for counterfactuals and individual treatment effects. Existing methods suffer from a significant coverage deficit even in simple models.
  arXiv  Detail & Related papers  (2020-06-11T01:03:32Z)

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.