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Joint structure learning and causal effect estimation for categorical graphical models.

Federico Castelletti1, Guido Consonni1, Marco L Della Vedova2

  • 1Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Largo Gemelli 1, Milan 20123, Italy.

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Summary
This summary is machine-generated.

This study introduces a new method to estimate causal effects in complex systems with categorical variables. The approach accurately measures intervention impacts by considering uncertainty in both the data structure and model parameters.

Keywords:
Bayesian inferencecategorical datacausal inferencedirected acyclic graphreversible jump Markov chain Monte Carlo

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Area of Science:

  • Causal inference
  • Statistical modeling
  • Biostatistics

Background:

  • Evaluating causal effects in multivariate systems with categorical variables is challenging.
  • Complex interdependencies between lifestyle, health features, and risk factors influence disease outcomes.
  • Existing methods often struggle to account for uncertainty in both the data structure and model parameters.

Purpose of the Study:

  • To develop a novel method for estimating causal effects in multivariate categorical settings.
  • To accurately assess the impact of external manipulations on an outcome of interest.
  • To account for uncertainty in the dependence structure (represented by directed acyclic graphs) and model parameters.

Main Methods:

  • Proposed a Markov chain Monte Carlo (MCMC) algorithm.
  • Utilized an efficient reversible-jump proposal scheme.
  • Targeted the joint posterior distribution over directed acyclic graphs (DAGs) and their parameters.

Main Results:

  • The proposed method demonstrated superior estimation accuracy compared to state-of-the-art procedures.
  • Extensive simulation studies validated the effectiveness of the algorithm.
  • The methodology was successfully applied to a real-world dataset on student mental health.

Conclusions:

  • The novel MCMC approach effectively estimates causal effects while accounting for structural and parametric uncertainty.
  • This method offers improved accuracy for analyzing complex multivariate categorical data.
  • The application to depression and anxiety data highlights its utility in public health research.