Patrick Burauel | Searcharxiv

arXiv2024

Controlling for discrete unmeasured confounding in nonlinear causal models

Unmeasured confounding is a major challenge for identifying causal relationships from non-experimental data. Here, we propose a method that can accommodate unmeasured discrete confounding. Extending recent identifiability results in deep latent variable models, we show theoretically that confounding can be detected and corrected under the assumption that the observed data is a piecewise affine transformation of a latent Gaussian mixture model and that the identity of the mixture components is confounded. We provide a flow-based algorithm to estimate this model and perform deconfounding. Experimental results on synthetic and real-world data provide support for the effectiveness of our approach.

cs.LG↗

arXiv2021

Testability of Reverse Causality Without Exogenous Variation

This paper shows that testability of reverse causality is possible even in the absence of exogenous variation, such as in the form of instrumental variables. Instead of relying on exogenous variation, we achieve testability by imposing relatively weak model restrictions and exploiting that a dependence of residual and purported cause is informative about the causal direction. Our main assumption is that the true functional relationship is nonlinear and that error terms are additively separable. We extend previous results by incorporating control variables and allowing heteroskedastic errors. We build on reproducing kernel Hilbert space (RKHS) embeddings of probability distributions to test conditional independence and demonstrate the efficacy in detecting the causal direction in both Monte Carlo simulations and an application to German survey data.

econ.EM↗