no code implementations • 4 Oct 2021 • Martin Emil Jakobsen
This thesis contributes to the research areas concerning the estimation of causal effects, causal structure learning, and distributionally robust (out-of-distribution generalizing) prediction methods.
1 code implementation • 19 Aug 2021 • Martin Emil Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters
Furthermore, we study the identifiability gap, which quantifies how much better the true causal model fits the observational distribution, and prove that it is lower bounded by local properties of the causal model.
1 code implementation • 12 Jun 2020 • Rune Christiansen, Niklas Pfister, Martin Emil Jakobsen, Nicola Gnecco, Jonas Peters
We introduce the formal framework of distribution generalization that allows us to analyze the above problem in partially observed nonlinear models for both direct interventions on $X$ and interventions that occur indirectly via exogenous variables $A$.
Methodology Primary 62Gxx, secondary 62G35, 62G08, 62D20
1 code implementation • 7 May 2020 • Martin Emil Jakobsen, Jonas Peters
While causal models are robust in that they are prediction optimal under arbitrarily strong interventions, they may not be optimal when the interventions are bounded.