Bayesian causal inference via probabilistic program synthesis [article]

Sam Witty, Alexander Lew, David Jensen, Vikash Mansinghka
2019 arXiv   pre-print
Causal inference can be formalized as Bayesian inference that combines a prior distribution over causal models and likelihoods that account for both observations and interventions. We show that it is possible to implement this approach using a sufficiently expressive probabilistic programming language. Priors are represented using probabilistic programs that generate source code in a domain specific language. Interventions are represented using probabilistic programs that edit this source code
more » ... o modify the original generative process. This approach makes it straightforward to incorporate data from atomic interventions, as well as shift interventions, variance-scaling interventions, and other interventions that modify causal structure. This approach also enables the use of general-purpose inference machinery for probabilistic programs to infer probable causal structures and parameters from data. This abstract describes a prototype of this approach in the Gen probabilistic programming language.
arXiv:1910.14124v1 fatcat:ywbfuyq4cjfzhjrpvqf4tqjhbi