Skip to yearly menu bar Skip to main content


Poster

Causal Modeling with Stationary Diffusions

Lars Lorch · Andreas Krause · Bernhard Schölkopf

MR1 & MR2 - Number 120
[ ]
Fri 3 May 8 a.m. PDT — 8:30 a.m. PDT
 
Oral presentation: Oral: Causality
Fri 3 May 2:30 a.m. PDT — 3:30 a.m. PDT

Abstract:

We develop a novel approach towards causal inference. Rather than structural equations over a causal graph, we learn stochastic differential equations (SDEs) whose stationary densities model a system's behavior under interventions. These stationary diffusion models do not require the formalism of causal graphs, let alone the common assumption of acyclicity. We show that in several cases, they generalize to unseen interventions on their variables, often better than classical approaches. Our inference method is based on a new theoretical result that expresses a stationarity condition on the diffusion's generator in a reproducing kernel Hilbert space. The resulting kernel deviation from stationarity (KDS) is an objective function of independent interest.

Live content is unavailable. Log in and register to view live content