Sample-Specific Root Causal Inference with Latent Variables

• URL: http://arxiv.org/abs/2210.15340v1
• Date: Thu, 27 Oct 2022 11:33:26 GMT
• Title: Sample-Specific Root Causal Inference with Latent Variables
• Authors: Eric V. Strobl, Thomas A. Lasko
• Abstract summary: Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted outcome. We rigorously quantified predictivity using Shapley values. We introduce a corresponding procedure called Extract Errors with Latents (EEL) for recovering the error terms up to contamination.
• Score: 10.885111578191564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted outcome. In prior work, we defined sample-specific root causes of disease using exogenous error terms that predict a diagnosis in a structural equation model. We rigorously quantified predictivity using Shapley values. However, the associated algorithms for inferring root causes assume no latent confounding. We relax this assumption by permitting confounding among the predictors. We then introduce a corresponding procedure called Extract Errors with Latents (EEL) for recovering the error terms up to contamination by vertices on certain paths under the linear non-Gaussian acyclic model. EEL also identifies the smallest sets of dependent errors for fast computation of the Shapley values. The algorithm bypasses the hard problem of estimating the underlying causal graph in both cases. Experiments highlight the superior accuracy and robustness of EEL relative to its predecessors.

Related papers

• Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis [8.309366167066278]
  Heuristic attribution methods, while helpful, often fail to capture true causal relationships, leading to inaccurate error attributions. We introduce the Causal-Discovery-based Root-Cause Analysis (CD-RCA) method that estimates causal relationships between the prediction error and the explanatory variables. By simulating synthetic error data, CD-RCA can identify variable contributions to outliers in prediction errors by Shapley values.
  arXiv  Detail & Related papers  (2024-11-11T13:48:13Z)
• Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding [1.6932009464531739]
  We consider linear non-Gaussian structural equation models that involve latent confounding. In this setting, the causal structure is identifiable, but, in general, it is not possible to identify the specific causal effects.
  arXiv  Detail & Related papers  (2024-08-09T07:24:12Z)
• 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 Patient-Specific Root Causes with the Heteroscedastic Noise Model [10.885111578191564]
  We focus on identifying patient-specific root causes of disease, which we equate to the sample-specific predictivity of the error terms in a structural equation model. A customized algorithm called Generalized Root Causal Inference (GRCI) is used to extract the error terms correctly.
  arXiv  Detail & Related papers  (2022-05-25T23:51:31Z)
• Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold Regression [2.520538806201793]
  In traditional logistic regression models, the link function is often assumed to be linear and continuous in predictors. We consider a threshold model that all continuous features are discretized into ordinal levels, which further determine the binary responses. We find the lasso model is well suited in the problem of early detection and prediction for chronic disease like diabetes.
  arXiv  Detail & Related papers  (2022-02-17T04:16:40Z)
• Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression [14.493176427999028]
  We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE) with linear regression models. We show that the T-learner fails to achieve the consistency except the random assignment, while the IPW-learner converges the risk to zero if the propensity score is known.
  arXiv  Detail & Related papers  (2022-02-10T18:51:52Z)
• Variance Minimization in the Wasserstein Space for Invariant Causal Prediction [72.13445677280792]
  In this work, we show that the approach taken in ICP may be reformulated as a series of nonparametric tests that scales linearly in the number of predictors. Each of these tests relies on the minimization of a novel loss function that is derived from tools in optimal transport theory. We prove under mild assumptions that our method is able to recover the set of identifiable direct causes, and we demonstrate in our experiments that it is competitive with other benchmark causal discovery algorithms.
  arXiv  Detail & Related papers  (2021-10-13T22:30:47Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Disentangling Observed Causal Effects from Latent Confounders using Method of Moments [67.27068846108047]
  We provide guarantees on identifiability and learnability under mild assumptions. We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
  arXiv  Detail & Related papers  (2021-01-17T07:48:45Z)
• Causal Expectation-Maximisation [70.45873402967297]
  We show that causal inference is NP-hard even in models characterised by polytree-shaped graphs. We introduce the causal EM algorithm to reconstruct the uncertainty about the latent variables from data about categorical manifest variables. We argue that there appears to be an unnoticed limitation to the trending idea that counterfactual bounds can often be computed without knowledge of the structural equations.
  arXiv  Detail & Related papers  (2020-11-04T10:25:13Z)
• On the Convergence Rate of Projected Gradient Descent for a Back-Projection based Objective [58.33065918353532]
  We consider a back-projection based fidelity term as an alternative to the common least squares (LS) We show that using the BP term, rather than the LS term, requires fewer iterations of optimization algorithms.
  arXiv  Detail & Related papers  (2020-05-03T00:58:23Z)

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.