Posts tagged do-operator

Counterfactual generation using pymc do-operator

In the realm of data science and analytics, understanding the causal relationships between variables is paramount. While traditional statistical methods have provided insights into these relationships, the advent of probabilistic programming has ushered in a new era of causal analysis. In this article, we will explore the power of counterfactuals in causal analysis using the PyMC framework, with a special focus on the “do-operator.” Counterfactuals are essentially “what-if” scenarios that allow us to understand the potential outcomes had a different action been taken or a different condition been present. By leveraging the PyMC framework and its “do-operator,” we can programmatically simulate these scenarios, giving us a deeper understanding of the relationships between predictors and target variables.

Read more ...


Interventional distributions and graph mutation with the do-operator

PyMC is a pivotal component of the open source Bayesian statistics ecosystem. It helps solve real problems across a wide range of industries and academic research areas every day. And it has gained this level of utility by being accessible, powerful, and practically useful at solving Bayesian statistical inference problems.

Read more ...