Position: Probabilistic Modelling is Sufficient for Causal Inference
from Best AI papers explained Hosted by Enoch H. Kang
This paper argues that probabilistic modelling is sufficient for causal inference, challenging the belief that specialized causal notations like the "do-operator" are strictly necessary. By advocating for a "write down the probability of everything" approach, the authors demonstrate that interventional and counterfactual questions can be solved using standard **Bayesian Networks** and joint distributions. They reinterpret traditional causal tools, such as **Structural Causal Models**, as useful syntactic shorthands rather than distinct mathematical requirements. The text suggests that the perceived gap between statistics and causality stems from a **semantic confusion** that unnecessarily narrows the definition of statistical inference. Ultimately, the authors promote a **unified framework** where causal reasoning is treated as a flexible application of existing probabilistic principles.
Embed this episode
No transcript yet.