Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments

• URL: http://arxiv.org/abs/2506.11756v1
• Date: Fri, 13 Jun 2025 13:11:37 GMT
• Title: Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
• Authors: Yaroslav Kivva, Sina Akbari, Saber Salehkaleybar, Negar Kiyavash,
• Abstract summary: We investigate the estimation of the causal effect of a treatment variable on an outcome in the presence of a latent confounder.<n>We propose a moment-based algorithm for estimating the causal effect as long as only a single parameter of the data-generating mechanism varies across environments.
• Score: 24.51302080975025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the estimation of the causal effect of a treatment variable on an outcome in the presence of a latent confounder. We first show that the causal effect is identifiable under certain conditions when data is available from multiple environments, provided that the target causal effect remains invariant across these environments. Secondly, we propose a moment-based algorithm for estimating the causal effect as long as only a single parameter of the data-generating mechanism varies across environments -- whether it be the exogenous noise distribution or the causal relationship between two variables. Conversely, we prove that identifiability is lost if both exogenous noise distributions of both the latent and treatment variables vary across environments. Finally, we propose a procedure to identify which parameter of the data-generating mechanism has varied across the environments and evaluate the performance of our proposed methods through experiments on synthetic data.

Related papers

• Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants [20.751445296400316]
  This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants.<n>We prove that causal effects are identifiable with a single proxy or instrument and provide corresponding estimation methods.
  arXiv  Detail & Related papers  (2025-06-05T16:14:35Z)
• Moments of Causal Effects [12.126945643201136]
  This work provides definitions, identification theorems, and bounds for moments and product moments of causal effects to analyze their distribution and relationships.<n>We conduct experiments to illustrate the estimation of the moments of causal effects from finite samples and demonstrate their practical application using a real-world medical dataset.
  arXiv  Detail & Related papers  (2025-05-08T06:09:05Z)
• Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables [15.105594376616253]
  Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science.<n>We propose a novel local learning approach for covariate selection in nonparametric causal effect estimation.<n>We validate our algorithm through extensive experiments on both synthetic and real-world data.
  arXiv  Detail & Related papers  (2024-11-25T12:08:54Z)
• A Bayesian Classification Trees Approach to Treatment Effect Variation with Noncompliance [0.5356944479760104]
  Estimating varying treatment effects in randomized trials with noncompliance is inherently challenging. Existing flexible machine learning methods are highly sensitive to the weak instruments problem. We present a Bayesian Causal Forest model for binary response variables in scenarios with noncompliance.
  arXiv  Detail & Related papers  (2024-08-14T18:33:55Z)
• 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)
• Effective Causal Discovery under Identifiable Heteroscedastic Noise Model [45.98718860540588]
  Causal DAG learning has recently achieved promising performance in terms of both accuracy and efficiency. We propose a novel formulation for DAG learning that accounts for the variation in noise variance across variables and observations. We then propose an effective two-phase iterative DAG learning algorithm to address the increasing optimization difficulties.
  arXiv  Detail & Related papers  (2023-12-20T08:51:58Z)
• Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data [27.38242139321935]
  We study the problem of identifying the unknown intervention targets in structural causal models. In the presence of latent confounders, the intervention targets among the observed variables cannot be determined uniquely. Our approach improves upon the state of the art as the returned candidate set is always a subset of the true intervention targets.
  arXiv  Detail & Related papers  (2023-12-11T03:31:54Z)
• 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.<n>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)
• A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
  We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
  arXiv  Detail & Related papers  (2023-06-08T09:40:28Z)
• 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)
• Identifying Weight-Variant Latent Causal Models [82.14087963690561]
  We find that transitivity acts as a key role in impeding the identifiability of latent causal representations. Under some mild assumptions, we can show that the latent causal representations can be identified up to trivial permutation and scaling. We propose a novel method, termed Structural caUsAl Variational autoEncoder, which directly learns latent causal representations and causal relationships among them.
  arXiv  Detail & Related papers  (2022-08-30T11:12:59Z)
• Differentiable Invariant Causal Discovery [106.87950048845308]
  Learning causal structure from observational data is a fundamental challenge in machine learning. This paper proposes Differentiable Invariant Causal Discovery (DICD) to avoid learning spurious edges and wrong causal directions. Extensive experiments on synthetic and real-world datasets verify that DICD outperforms state-of-the-art causal discovery methods up to 36% in SHD.
  arXiv  Detail & Related papers  (2022-05-31T09:29:07Z)

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.