Search Results for author: Martin Emil Jakobsen

Found 4 papers, 3 papers with code

Causality and Generalizability: Identifiability and Learning Methods

no code implementations4 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.

Econometrics Time Series Analysis +1

Structure Learning for Directed Trees

1 code implementation19 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.

valid

A causal framework for distribution generalization

1 code implementation12 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

Distributional robustness of K-class estimators and the PULSE

1 code implementation7 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.

Causal Discovery valid

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