Identifying Conditional Causal Effects
This addresses the challenge of causal inference in fields like epidemiology or social sciences where controlled experiments are infeasible, offering a systematic method for researchers.
The paper tackles the problem of identifying conditional causal effects from observational data and causal assumptions encoded in directed acyclic graphs with unobserved variables, providing a polynomial-time procedure to express these effects in terms of the observed joint distribution.
This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects between two sets of variables conditioned on some other variables, in time polynomial in the number of variables in the graph. The identifiable conditional causal effects are expressed in terms of the observed joint distribution.