Causal assumptions : some responses to Nancy Cartwright

The theories of causality put forward by Pearl and the Spirtes-Glymour-Scheines group have entered the mainstream of statistical thinking. These theories show that under ideal conditions, causal relationships can be inferred from purely statistical observational data. Nancy Cartwright advances certain arguments against these causal inference algorithms: the well-known “factory example” argument against the Causal Markov condition and an argument against faithfulness. We point to the dependence of the first argument on undefined categories external to the technical apparatus of causal inference algorithms. We acknowledge the possible practical implication of her second argument, yet we maintain, with respect to both arguments, that this variety of causal inference, if not universal, is nonetheless eminently useful. Cartwright argues against assumptions that are essential not only to causal inference algorithms but to causal inference generally, even if, as she contends, they are not without exception and that the same is true of other, likewise essential, assumptions. We indicate that causal inference is an iterative process and that causal inference algorithms assist, rather than replace, that process as performed by human beings