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Michaela Hardt

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3 papers

2 author rows

AAAI Conference 2025 Conference Paper

Toward Falsifying Causal Graphs Using a Permutation-Based Test

Elias Eulig
Atalanti A. Mastakouri
Patrick Blöbaum
Michaela Hardt
Dominik Janzing

Understanding causal relationships among the variables of a system is paramount to explain and control its behavior. For many real-world systems, however, the true causal graph is not readily available and one must resort to predictions made by algorithms or domain experts. Therefore, metrics that quantitatively assess the goodness of a causal graph provide helpful checks before using it in downstream tasks. Existing metrics provide an absolute number of inconsistencies between the graph and the observed data, and without a baseline, practitioners are left to answer the hard question of how many such inconsistencies are acceptable or expected. Here, we propose a novel consistency metric by constructing a baseline through node permutations. By comparing the number of inconsistencies with those on the baseline, we derive an interpretable metric that captures whether the graph is significantly better than random. Evaluating on both simulated and real data sets from various domains, including biology and cloud monitoring, we demonstrate that the true graph is not falsified by our metric, whereas the wrong graphs given by a hypothetical user are likely to be falsified.

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UAI Conference 2023 Conference Paper

Causal information splitting: Engineering proxy features for robustness to distribution shifts

Bijan Mazaheri
Atalanti-Anastasia Mastakouri
Dominik Janzing
Michaela Hardt

Statistical prediction models are often trained on data that is drawn from different probability distributions than their eventual use cases. One approach to proactively prepare for these shifts harnesses the intuition that causal mechanisms should remain invariant between environments. Here we focus on a challenging setting in which the causal and anticausal variables of the target are unobserved. Leaning on information theory, we develop feature selection and engineering techniques for the observed downstream variables that act as proxies. We identify proxies that help to build stable models and moreover utilize auxiliary training tasks to extract stability-enhancing information from proxies. We demonstrate the effectiveness of our techniques on synthetic and real data.

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UAI Conference 2022 Conference Paper

Causal forecasting: generalization bounds for autoregressive models

Leena Chennuru Vankadara
Philipp Michael Faller
Michaela Hardt
Lenon Minorics
Debarghya Ghoshdastidar
Dominik Janzing

Despite the increasing relevance of forecasting methods, causal implications of these algorithms remain largely unexplored. This is concerning considering that, even under simplifying assumptions such as causal sufficiency, the statistical risk of a model can differ significantly from its causal risk. Here, we study the problem of causal generalization—generalizing from the observational to interventional distributions—in forecasting. Our goal is to find answers to the question: How does the efficacy of an autoregressive (VAR) model in predicting statistical associations compare with its ability to predict under interventions? To this end, we introduce the framework of causal learning theory for forecasting. Using this framework, we obtain a characterization of the difference between statistical and causal risks, which helps identify sources of divergence between them. Under causal sufficiency, the problem of causal generalization amounts to learning under covariate shifts albeit with additional structure (restriction to interventional distributions under the VAR model). This structure allows us to obtain uniform convergence bounds on causal generalizability for the class of VAR models. To the best of our knowledge, this is the first work that provides theoretical guarantees for causal generalization in the time-series setting.

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