Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms

• URL: http://arxiv.org/abs/2502.06231v1
• Date: Mon, 10 Feb 2025 08:05:44 GMT
• Title: Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
• Authors: Rickard K. A. Karlsson, Jesse H. Krijthe,
• Abstract summary: We propose an algorithm that can falsify the assumption of no unmeasured confounding in a setting with observational data. Our proposed falsification strategy leverages a key observation that unmeasured confounding can cause observed causal mechanisms to appear dependent. We show that our method is able to efficiently detect confounding on both simulated and real-world data.
• Score: 0.6906005491572399
• License:
• Abstract: A major challenge in estimating treatment effects in observational studies is the reliance on untestable conditions such as the assumption of no unmeasured confounding. In this work, we propose an algorithm that can falsify the assumption of no unmeasured confounding in a setting with observational data from multiple heterogeneous sources, which we refer to as environments. Our proposed falsification strategy leverages a key observation that unmeasured confounding can cause observed causal mechanisms to appear dependent. Building on this observation, we develop a novel two-stage procedure that detects these dependencies with high statistical power while controlling false positives. The algorithm does not require access to randomized data and, in contrast to other falsification approaches, functions even under transportability violations when the environment has a direct effect on the outcome of interest. To showcase the practical relevance of our approach, we show that our method is able to efficiently detect confounding on both simulated and real-world data.

Related papers

• Identifiability Guarantees for Causal Disentanglement from Purely Observational Data [10.482728002416348]
  Causal disentanglement aims to learn about latent causal factors behind data. Recent advances establish identifiability results assuming that interventions on (single) latent factors are available. We provide a precise characterization of latent factors that can be identified in nonlinear causal models.
  arXiv  Detail & Related papers  (2024-10-31T04:18:29Z)
• Assumption violations in causal discovery and the robustness of score matching [38.60630271550033]
  This paper extensively benchmarks the empirical performance of recent causal discovery methods on observational i.i.d. data. We show that score matching-based methods demonstrate surprising performance in the false positive and false negative rate of the inferred graph. We hope this paper will set a new standard for the evaluation of causal discovery methods.
  arXiv  Detail & Related papers  (2023-10-20T09:56:07Z)
• 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)
• A Double Machine Learning Approach to Combining Experimental and Observational Data [59.29868677652324]
  We propose a double machine learning approach to combine experimental and observational studies. Our framework tests for violations of external validity and ignorability under milder assumptions.
  arXiv  Detail & Related papers  (2023-07-04T02:53:11Z)
• 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)
• 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)
• Valid Inference After Causal Discovery [73.87055989355737]
  We develop tools for valid post-causal-discovery inference. We show that a naive combination of causal discovery and subsequent inference algorithms leads to highly inflated miscoverage rates.
  arXiv  Detail & Related papers  (2022-08-11T17:40:45Z)
• Detecting hidden confounding in observational data using multiple environments [0.81585306387285]
  We present a theory for testable conditional independencies that are only absent when there is hidden confounding. In most cases, the proposed procedure correctly predicts the presence of hidden confounding.
  arXiv  Detail & Related papers  (2022-05-27T12:20:09Z)
• The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions [0.0]
  In certain settings, such as genomics, we may have data from heterogeneous study conditions, with soft (partial) interventions only pertaining to a subset of the study variables. We define the interventional BGe score for a mixture of observational and interventional data, where the targets and effects of intervention may be unknown.
  arXiv  Detail & Related papers  (2022-05-05T12:32:08Z)
• Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
  Key challenge for robotic systems is to figure out the behavior of another agent. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally. We propose equipping robots with the necessary tools to conduct observational studies on people.
  arXiv  Detail & Related papers  (2022-01-27T22:15:56Z)
• 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.