Causal Discovery via Conditional Independence Testing with Proxy Variables

• URL: http://arxiv.org/abs/2305.05281v3
• Date: Thu, 2 May 2024 01:54:54 GMT
• Title: Causal Discovery via Conditional Independence Testing with Proxy Variables
• Authors: Mingzhou Liu, Xinwei Sun, Yu Qiao, Yizhou Wang,
• Abstract summary: The presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing. We propose a novel hypothesis-testing procedure that can effectively examine the existence of the causal relationship over continuous variables.
• Score: 35.3493980628004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing commonly employed in constraint-based causal discovery for identifying causal relations. To address this issue, existing methods introduced proxy variables to adjust for the bias caused by unobserveness. However, these methods were either limited to categorical variables or relied on strong parametric assumptions for identification. In this paper, we propose a novel hypothesis-testing procedure that can effectively examine the existence of the causal relationship over continuous variables, without any parametric constraint. Our procedure is based on discretization, which under completeness conditions, is able to asymptotically establish a linear equation whose coefficient vector is identifiable under the causal null hypothesis. Based on this, we introduce our test statistic and demonstrate its asymptotic level and power. We validate the effectiveness of our procedure using both synthetic and real-world data.

Related papers

• Simultaneous inference for generalized linear models with unmeasured confounders [0.0]
  We propose a unified statistical estimation and inference framework that harnesses structures and integrates linear projections into three key stages. We show effective Type-I error control of $z$-tests as sample and response sizes approach infinity.
  arXiv  Detail & Related papers  (2023-09-13T18:53:11Z)
• Self-Compatibility: Evaluating Causal Discovery without Ground Truth [28.72650348646176]
  We propose a novel method for falsifying the output of a causal discovery algorithm in the absence of ground truth. Our key insight is that while statistical learning seeks stability across subsets of data points, causal learning should seek stability across subsets of variables. We prove that detecting incompatibilities can falsify wrongly inferred causal relations due to violation of assumptions or errors from finite sample effects.
  arXiv  Detail & Related papers  (2023-07-18T18:59:42Z)
• 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)
• 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)
• Nonparametric Conditional Local Independence Testing [69.31200003384122]
  Conditional local independence is an independence relation among continuous time processes. No nonparametric test of conditional local independence has been available. We propose such a nonparametric test based on double machine learning.
  arXiv  Detail & Related papers  (2022-03-25T10:31:02Z)
• On Testability of the Front-Door Model via Verma Constraints [7.52579126252489]
  Front-door criterion can be used to identify and compute causal effects despite unmeasured confounders. Key assumptions -- the existence of a variable that fully mediates the effect of the treatment on the outcome, and which simultaneously does not suffer from similar issues of confounding -- are often deemed implausible. We show that under mild conditions involving an auxiliary variable, the assumptions encoded in the front-door model may be tested via generalized equality constraints.
  arXiv  Detail & Related papers  (2022-03-01T00:38:29Z)
• Typing assumptions improve identification in causal discovery [123.06886784834471]
  Causal discovery from observational data is a challenging task to which an exact solution cannot always be identified. We propose a new set of assumptions that constrain possible causal relationships based on the nature of the variables.
  arXiv  Detail & Related papers  (2021-07-22T14:23:08Z)
• 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)
• Stable Prediction via Leveraging Seed Variable [73.9770220107874]
  Previous machine learning methods might exploit subtly spurious correlations in training data induced by non-causal variables for prediction. We propose a conditional independence test based algorithm to separate causal variables with a seed variable as priori, and adopt them for stable prediction. Our algorithm outperforms state-of-the-art methods for stable prediction.
  arXiv  Detail & Related papers  (2020-06-09T06:56:31Z)

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.