This paper is concerned with estimating the effects of actions from causal assumptions, represented concisely as a directed graph, and statistical knowledge, given as a probability distribution. In this talk, I will present the ID algorithm, talk about necessary and sufficient graphical conditions for the cases when the causal effect of an arbitrary set of variables on another arbitrary set can be determined uniquely from the available information.
