observational and experimental data

We show that one can reduce a causal discovery problem on several different observational and experimental settings to a problem on a single distribution with the addition of several context variables. We call this approach Joint Causal Inference and show it can be combined with many algorithms, e.g. also algorithms that can deal with cycles, latent confounders and selection bias.