Search Results for author: Stephan Bongers

Found 7 papers, 1 papers with code

Beyond Structural Causal Models: Causal Constraints Models

no code implementations16 May 2018 Tineke Blom, Stephan Bongers, Joris M. Mooij

Structural Causal Models (SCMs) provide a popular causal modeling framework.

Causal Modeling of Dynamical Systems

no code implementations23 Mar 2018 Stephan Bongers, Tineke Blom, Joris M. Mooij

We introduce the formal framework of structural dynamical causal models (SDCMs) that explicates the causal semantics of the system's components as part of the model.

Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions

1 code implementation NeurIPS 2018 Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij

An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ.

Causal Inference Domain Adaptation

Foundations of Structural Causal Models with Cycles and Latent Variables

no code implementations18 Nov 2016 Stephan Bongers, Patrick Forré, Jonas Peters, Joris M. Mooij

In this paper, we investigate SCMs in a more general setting, allowing for the presence of both latent confounders and cycles.

counterfactual

From Deterministic ODEs to Dynamic Structural Causal Models

no code implementations29 Aug 2016 Paul K. Rubenstein, Stephan Bongers, Bernhard Schoelkopf, Joris M. Mooij

Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood.

Cannot find the paper you are looking for? You can Submit a new open access paper.