The scope of this paper is a multivariate setting involving categorical variables. Following an external manipulation of one variable, the goal is to evaluate the causal effect on an outcome of interest. A typical scenario involves a system of variables representing lifestyle, physical and mental features, symptoms, and risk factors, with the outcome being the presence or absence of a disease. These variables are interconnected in complex ways, allowing the effect of an intervention to propagate through multiple paths. A distinctive feature of our approach is the estimation of causal effects while accounting for uncertainty in both the dependence structure, which we represent through a directed acyclic graph (DAG), and the DAG-model parameters. Specifically, we propose a Markov chain Monte Carlo algorithm that targets the joint posterior over DAGs and parameters, based on an efficient reversible-jump proposal scheme. We validate our method through extensive simulation studies and demonstrate that it outperforms current state-of-the-art procedures in terms of estimation accuracy. Finally, we apply our methodology to analyze a dataset on depression and anxiety in undergraduate students.

Castelletti, F., Consonni, G., Della Vedova, M. (2024). Joint structure learning and causal effect estimation for categorical graphical models. BIOMETRICS, 80(3) [10.1093/biomtc/ujae067].

Joint structure learning and causal effect estimation for categorical graphical models

Castelletti F.
;
2024

Abstract

The scope of this paper is a multivariate setting involving categorical variables. Following an external manipulation of one variable, the goal is to evaluate the causal effect on an outcome of interest. A typical scenario involves a system of variables representing lifestyle, physical and mental features, symptoms, and risk factors, with the outcome being the presence or absence of a disease. These variables are interconnected in complex ways, allowing the effect of an intervention to propagate through multiple paths. A distinctive feature of our approach is the estimation of causal effects while accounting for uncertainty in both the dependence structure, which we represent through a directed acyclic graph (DAG), and the DAG-model parameters. Specifically, we propose a Markov chain Monte Carlo algorithm that targets the joint posterior over DAGs and parameters, based on an efficient reversible-jump proposal scheme. We validate our method through extensive simulation studies and demonstrate that it outperforms current state-of-the-art procedures in terms of estimation accuracy. Finally, we apply our methodology to analyze a dataset on depression and anxiety in undergraduate students.
Articolo in rivista - Articolo scientifico
Bayesian inference; categorical data; causal inference; directed acyclic graph; reversible jump Markov chain Monte Carlo;
English
29-lug-2024
2024
80
3
ujae067
reserved
Castelletti, F., Consonni, G., Della Vedova, M. (2024). Joint structure learning and causal effect estimation for categorical graphical models. BIOMETRICS, 80(3) [10.1093/biomtc/ujae067].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/503540
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