Probabilistic Modelling is Sufficient for Causal Inference
This clarifies a foundational confusion in machine learning about the necessity of causal-specific tools, potentially simplifying approaches for researchers and practitioners.
The paper argues that causal inference questions can be addressed using standard probabilistic modeling without specialized causal frameworks, demonstrating this through examples and reinterpreting causal tools as derived from probabilistic methods.
Causal inference is a key research area in machine learning, yet confusion reigns over the tools needed to tackle it. There are prevalent claims in the machine learning literature that you need a bespoke causal framework or notation to answer causal questions. In this paper, we want to make it clear that you \emph{can} answer any causal inference question within the realm of probabilistic modelling and inference, without causal-specific tools or notation. Through concrete examples, we demonstrate how causal questions can be tackled by writing down the probability of everything. Lastly, we reinterpret causal tools as emerging from standard probabilistic modelling and inference, elucidating their necessity and utility.