The Institute for Statistics and Mathematics is pleased to invite you to the next research seminar, taking place on campus:

Lucas Kook (Department of Mathematical Sciences, University of Copenhagen):
Invariance and Causality in Transformation Models: Causal Feature Selection and Robust Prediction
Friday, May 5, 2023, 11:00 am, Room D4.4.008

Abstract: Discovering causal relationships from observational data is a fundamental yet challenging task. For some applications, it may suffice to learn the causal drivers of a given response variable instead of the entire causal graph. Invariant causal prediction (ICP) is a method for causal feature selection which requires data from heterogeneous settings. ICP assumes that the mechanism of the response is the same in all settings and exploits invariance of the conditional distribution of the response given its parents across those settings. The original formulation of ICP for linear models has been extended to general independent additive noise models and to nonparameteric settings using conditional independence testing. However, additive noise models are not suitable for applications in which the response is not measured on a continuous scale, but rather reflects categories or counts, while nonparametric conditional independence testing often suffers from low power. To bridge this gap, we develop ICP for continuous, categorical, count-type, and uninformatively censored responses in parametric transformation models. We propose procedures for testing invariance based on score residuals, establish coverage guarantees and empirically show gains in power over nonparametric alternatives when the model is correctly specified. Our proposed method is implemented in the R~package 'tramicp', which we demonstrate on data from a randomized controlled trial. Lastly, we explore how invariance can aid in prediction under distribution shift when some assumptions of ICP, such as causal sufficiency, are violated.

We aim to stream all on-campus talks via Zoom. A direct link to the stream will be posted on our website.

For further information and the seminar schedule, please see:
www.wu.ac.at/en/statmath/research/resseminar