( Image credit: TCDF )
We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual predictions.
Standard causal discovery methods must fit a new model whenever they encounter samples from a new underlying causal graph.
We consider causal discovery from time series using conditional independence (CI) based network learning algorithms such as the PC algorithm.
The learned SSCM gives the specific causal knowledge for each individual as well as the general trend over the population.
Causal discovery witnessed significant progress over the past decades.
We show that the data generated from our simulator have similar statistics as real-world data.
In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect.
This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling.
We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the association between one or several features and a given outcome, conditional on a reduced feature set.