Causal Estimation through the Intervention of Variables in Causal Bayesian Networks

Causal Estimation through the Intervention of Variables in Causal Bayesian Networks

Presented at the 2023 BayesiaLab Conference.


The study of causality can be traced back 300 years; its origins are attributed to two thinkers of the epoch (Emmanuel Kant and David Hume). Both philosophical currents are opposed, though both have the same objective, to explain the origins of causal processes in the human brain.

Several disciplines have been studying causality from different prospects; two of these disciplines are Cognitive Psychology (CP) and Artificial Intelligence (AI). Despite these areas having their origins together, currently, they work separately.

There are multiple efforts to replicate and explain causality based on prior knowledge; from CP, there have been different proposals; its aim is to describe how to learn the natural cause-effect relationships, and from AI, there are multiple algorithms focused on estimating or predicting causality.

From IA, a method for learning causality is through the intervention of variables in Bayesian Networks (BN); however, this requires prior knowledge that indicates the cause-effect direction within the connections in the network. For the above, our proposal is a joint implementation to create Causal Bayesian Networks (CBN) from the fusion of two areas, the CP and IA. The principal goal is an integrated two algorithms for the construction of the graphical models (CBN) from the dataset that can learn the causal relationships, giving direction to the arcs within the network, such that indicate who is the cause and who is the effect. For the construction, we used the Rescorla-Wagner model in the first algorithm and the Power-PC theory for the second; both methods belong to the CP.

The results obtained with this approach have been encouraging, and the CBNs acquired can be used to intervene in variables and estimate causal probabilities. For comparison, we used traditional Bayesian Networks proposed by AI.

We have tested with real and repository datasets, comparing our networks with those drawn manually by the experts who have provided us with the data. In the beginning, only 14% of the BNs constructed with traditional AI algorithms could be used for intervention purposes. Most of the CBNs obtained with this proposed algorithm can be used for this purpose and reflect more cause-effect connections than those created with other AI algorithms.

Presentation Video

Presentation Slides

About the Presenter

Jenny Betsabé Vázquez-Aguirre was born in Veracruz, Mexico. She has a bachelor's degree in statistics and two master's degrees: one in Quality Management and the other in Artificial Intelligence.

She has worked as a data analyst, software tester, process manager, as well as statistical methodology teacher.

She is currently studying the last semester of a Ph.D. in Artificial Intelligence, in which she is working on causal analysis through Causal Bayesian Networks principally, but also, she is continually working on data analysis with machine learning.\

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