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David Kaltenpoth

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4 papers

2 author rows

UAI Conference 2023 Conference Paper

Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition

David Kaltenpoth
Jilles Vreeken

Causal sufficiency is a cornerstone assumption in causal discovery. It is, however, both unlikely to hold in practice as well as unverifiable. When it does not hold, existing methods struggle to return meaningful results. In this paper, we show how to discover the causal network over both observed and unobserved variables. Moreover, we show that the causal model is identifiable in the sparse linear Gaussian case. More generally, we extend the algorithmic Markov condition to include latent confounders. We propose a consistent score based on the Minimum Description Length principle to discover the full causal network, including latent confounders. Based on this score, we develop an effective algorithm that finds those sets of nodes for which the addition of a confounding factor $Z$ is most beneficial, then fits a new causal network over both observed as well as inferred latent variables.

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AAAI Conference 2023 Conference Paper

Identifying Selection Bias from Observational Data

David Kaltenpoth
Jilles Vreeken

Access to a representative sample from the population is an assumption that underpins all of machine learning. Selection effects can cause observations to instead come from a subpopulation, by which our inferences may be subject to bias. It is therefore important to know whether or not a sample is affected by selection effects. We study under which conditions we can identify selection bias and give results for both parametric and non-parametric families of distributions. Based on these results we develop two practical methods to determine whether or not an observed sample comes from a distribution subject to selection bias. Through extensive evaluation on synthetic and real world data we verify that our methods beat the state of the art both in detecting as well as characterizing selection bias.

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NeurIPS Conference 2023 Conference Paper

Learning Causal Models under Independent Changes

Sarah Mameche
David Kaltenpoth
Jilles Vreeken

In many scientific applications, we observe a system in different conditions in which its components may change, rather than in isolation. In our work, we are interested in explaining the generating process of such a multi-context system using a finite mixture of causal mechanisms. Recent work shows that this causal model is identifiable from data, but is limited to settings where the sparse mechanism shift hypothesis holds and only a subset of the causal conditionals change. As this assumption is not easily verifiable in practice, we study the more general principle that mechanism shifts are independent, which we formalize using the algorithmic notion of independence. We introduce an approach for causal discovery beyond partially directed graphs using Gaussian Process models, and give conditions under which we provably identify the correct causal model. In our experiments, we show that our method performs well in a range of synthetic settings, on realistic gene expression simulations, as well as on real-world cell signaling data.

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ICML Conference 2023 Conference Paper

Nonlinear Causal Discovery with Latent Confounders

David Kaltenpoth
Jilles Vreeken

Causal discovery, the task of discovering the causal graph over a set of observed variables $X_1, \ldots, X_m$, is a challenging problem. One of the cornerstone assumptions is that of causal sufficiency: that all common causes of all measured variables have been observed. When it does not hold, causal discovery algorithms making this assumption return networks with many spurious edges. In this paper, we propose a nonlinear causal model involving hidden confounders. We show that it is identifiable from only the observed data and propose an efficient method for recovering this causal model. At the heart of our approach is a variational autoencoder which parametrizes both the causal interactions between observed variables as well as the influence of the unobserved confounders. Empirically we show that it outperforms other state-of-the-art methods for causal discovery under latent confounding on synthetic and real-world data.

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