Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets

Understand

Scientific practice typically involves repeatedly studying a system, each time trying to unravel a different perspective.

• In each study, the scientist may take measurements under different experimental conditions (interventions, manipulations, perturbations) and measure different sets of quantities (variables).
• The result is a collection of heterogeneous data sets coming from different data distributions.
• In this work, we present algorithm COmbINE, which accepts a collection of data sets over overlapping variable sets under different experimental conditions; COmbINE then outputs a summary of all causal models indicating the invariant and variant structural characteristics of all models that simultaneously fit all of the input data sets.