Identification Methods With Arbitrary Interventional Distributions as Inputs

• URL: http://arxiv.org/abs/2004.01157v2
• Date: Wed, 15 Apr 2020 16:43:19 GMT
• Title: Identification Methods With Arbitrary Interventional Distributions as Inputs
• Authors: Jaron J. R. Lee, Ilya Shpitser
• Abstract summary: Causal inference quantifies cause-effect relationships by estimating counterfactual parameters from data. We use Single World Intervention Graphs and a nested factorization of models associated with mixed graphs to give a very simple view of existing identification theory for experimental data.
• Score: 8.185725740857595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal inference quantifies cause-effect relationships by estimating counterfactual parameters from data. This entails using \emph{identification theory} to establish a link between counterfactual parameters of interest and distributions from which data is available. A line of work characterized non-parametric identification for a wide variety of causal parameters in terms of the \emph{observed data distribution}. More recently, identification results have been extended to settings where experimental data from interventional distributions is also available. In this paper, we use Single World Intervention Graphs and a nested factorization of models associated with mixed graphs to give a very simple view of existing identification theory for experimental data. We use this view to yield general identification algorithms for settings where the input distributions consist of an arbitrary set of observational and experimental distributions, including marginal and conditional distributions. We show that for problems where inputs are interventional marginal distributions of a certain type (ancestral marginals), our algorithm is complete.

Related papers

• A Semiparametric Approach to Causal Inference [2.092897805817524]
  In causal inference, an important problem is to quantify the effects of interventions or treatments. In this paper, we employ a semiparametric density ratio model (DRM) to characterize the counterfactual distributions. Our model offers flexibility by avoiding strict parametric assumptions on the counterfactual distributions.
  arXiv  Detail & Related papers  (2024-11-01T18:03:38Z)
• An Overview of Causal Inference using Kernel Embeddings [14.298666697532838]
  Kernel embeddings have emerged as a powerful tool for representing probability measures in a variety of statistical inference problems. Main challenges include identifying causal associations and estimating the average treatment effect from observational data.
  arXiv  Detail & Related papers  (2024-10-30T07:23:34Z)
• Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data [1.0650780147044159]
  We address the goal of conducting inference about a smooth finite-dimensional parameter by utilizing individual-level data from various independent sources. Recent advancements have led to the development of a comprehensive theory capable of handling scenarios where different data sources align with, possibly distinct subsets of, conditional distributions of a single factorization of the joint target distribution. We extend the aforementioned comprehensive theory to allow for the fusion of individual-level data from sources aligned with conditional distributions that do not correspond to a single factorization of the target distribution.
  arXiv  Detail & Related papers  (2024-09-16T04:10:44Z)
• Synthetic Tabular Data Validation: A Divergence-Based Approach [8.062368743143388]
  Divergences quantify discrepancies between data distributions. Traditional approaches calculate divergences independently for each feature. We propose a novel approach that uses divergence estimation to overcome the limitations of marginal comparisons.
  arXiv  Detail & Related papers  (2024-05-13T15:07:52Z)
• Identifiable Latent Neural Causal Models [82.14087963690561]
  Causal representation learning seeks to uncover latent, high-level causal representations from low-level observed data. We determine the types of distribution shifts that do contribute to the identifiability of causal representations. We translate our findings into a practical algorithm, allowing for the acquisition of reliable latent causal representations.
  arXiv  Detail & Related papers  (2024-03-23T04:13:55Z)
• Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models [59.331993845831946]
  Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties. This paper provides the first theoretical study towards understanding the influence of guidance on diffusion models in the context of Gaussian mixture models.
  arXiv  Detail & Related papers  (2024-03-03T23:15:48Z)
• Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources [64.96984404868411]
  We address the problem of integrating data from multiple, possibly biased, observational and interventional studies. We show that the likelihood of the available data has no local maxima. We then show how the same approach can address the general case of multiple datasets.
  arXiv  Detail & Related papers  (2023-07-31T11:28:24Z)
• 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)
• Data thinning for convolution-closed distributions [2.299914829977005]
  We propose data thinning, an approach for splitting an observation into two or more independent parts that sum to the original observation. We show that data thinning can be used to validate the results of unsupervised learning approaches.
  arXiv  Detail & Related papers  (2023-01-18T02:47:41Z)
• Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data [64.96984404868411]
  We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm. The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources. It delivers interval approximations to counterfactual results, which collapse to points in the identifiable case.
  arXiv  Detail & Related papers  (2022-12-06T12:42:11Z)
• Asymptotic Analysis of an Ensemble of Randomly Projected Linear Discriminants [94.46276668068327]
  In [1], an ensemble of randomly projected linear discriminants is used to classify datasets. We develop a consistent estimator of the misclassification probability as an alternative to the computationally-costly cross-validation estimator. We also demonstrate the use of our estimator for tuning the projection dimension on both real and synthetic data.
  arXiv  Detail & Related papers  (2020-04-17T12:47:04Z)

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.