Heterogeneous Causal Discovery of Repeated Undesirable Health Outcomes

• URL: http://arxiv.org/abs/2503.11477v1
• Date: Fri, 14 Mar 2025 15:05:17 GMT
• Title: Heterogeneous Causal Discovery of Repeated Undesirable Health Outcomes
• Authors: Shishir Adhikari, Guido Muscioni, Mark Shapiro, Plamen Petrov, Elena Zheleva,
• Abstract summary: Causal discovery offers an alternative to conventional approaches by generating cause-and-effect hypotheses from observational data.<n>It often relies on strong or untestable assumptions, which can limit its practical application.<n>This work aims to make causal discovery more practical by considering multiple assumptions and identifying heterogeneous effects.
• Score: 8.16644941863291
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Understanding factors triggering or preventing undesirable health outcomes across patient subpopulations is essential for designing targeted interventions. While randomized controlled trials and expert-led patient interviews are standard methods for identifying these factors, they can be time-consuming and infeasible. Causal discovery offers an alternative to conventional approaches by generating cause-and-effect hypotheses from observational data. However, it often relies on strong or untestable assumptions, which can limit its practical application. This work aims to make causal discovery more practical by considering multiple assumptions and identifying heterogeneous effects. We formulate the problem of discovering causes and effect modifiers of an outcome, where effect modifiers are contexts (e.g., age groups) with heterogeneous causal effects. Then, we present a novel, end-to-end framework that incorporates an ensemble of causal discovery algorithms and estimation of heterogeneous effects to discover causes and effect modifiers that trigger or inhibit the outcome. We demonstrate that the ensemble approach improves robustness by enhancing recall of causal factors while maintaining precision. Our study examines the causes of repeat emergency room visits for diabetic patients and hospital readmissions for ICU patients. Our framework generates causal hypotheses consistent with existing literature and can help practitioners identify potential interventions and patient subpopulations to focus on.

Related papers

• Identifiable Latent Polynomial Causal Models Through the Lens of Change [82.14087963690561]
  Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability.
  arXiv  Detail & Related papers  (2023-10-24T07:46:10Z)
• Nonlinearity, Feedback and Uniform Consistency in Causal Structural Learning [0.8158530638728501]
  Causal Discovery aims to find automated search methods for learning causal structures from observational data. This thesis focuses on two questions in causal discovery: (i) providing an alternative definition of k-Triangle Faithfulness that (i) is weaker than strong faithfulness when applied to the Gaussian family of distributions, and (ii) under the assumption that the modified version of Strong Faithfulness holds.
  arXiv  Detail & Related papers  (2023-08-15T01:23:42Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects [36.358968799947924]
  Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations. We propose iCITRIS, a causal representation learning method that can handle instantaneous effects in temporal sequences. In experiments on three video datasets, iCITRIS accurately identifies the causal factors and their causal graph.
  arXiv  Detail & Related papers  (2022-06-13T13:56:40Z)
• Active Bayesian Causal Inference [72.70593653185078]
  We propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning. ABCI jointly infers a posterior over causal models and queries of interest. We show that our approach is more data-efficient than several baselines that only focus on learning the full causal graph.
  arXiv  Detail & Related papers  (2022-06-04T22:38:57Z)
• BaCaDI: Bayesian Causal Discovery with Unknown Interventions [118.93754590721173]
  BaCaDI operates in the continuous space of latent probabilistic representations of both causal structures and interventions. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.
  arXiv  Detail & Related papers  (2022-06-03T16:25:48Z)
• Causal Effect Estimation using Variational Information Bottleneck [19.6760527269791]
  Causal inference is to estimate the causal effect in a causal relationship when intervention is applied. We propose a method to estimate Causal Effect by using Variational Information Bottleneck (CEVIB)
  arXiv  Detail & Related papers  (2021-10-26T13:46:12Z)
• An introduction to causal reasoning in health analytics [2.199093822766999]
  We will try to highlight some of the drawbacks that may arise in traditional machine learning and statistical approaches to analyze the observational data. We will demonstrate the applications of causal inference in tackling some common machine learning issues.
  arXiv  Detail & Related papers  (2021-05-10T20:25:56Z)
• Causes of Effects: Learning individual responses from population data [23.593582720307207]
  We study the problem of individualization and its applications in medicine. For example, the probability of benefiting from a treatment concerns an individual having a favorable outcome if treated and an unfavorable outcome if untreated. We analyze and expand on existing research by applying bounds to the probability of necessity and sufficiency (PNS) along with graphical criteria and practical applications.
  arXiv  Detail & Related papers  (2021-04-28T12:38:11Z)
• ACRE: Abstract Causal REasoning Beyond Covariation [90.99059920286484]
  We introduce the Abstract Causal REasoning dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario. We notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning.
  arXiv  Detail & Related papers  (2021-03-26T02:42:38Z)
• 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)

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.