no code implementations • 18 Oct 2023 • Junaid Ali, Matthaeus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell
We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks.
no code implementations • 23 Apr 2023 • Yuchen Zhu, Kailash Budhathoki, Jonas Kuebler, Dominik Janzing
On the positive side, we show that cause-effect relations can be aggregated when the macro interventions are such that the distribution of micro states is the same as in the observational distribution; we term this natural macro interventions.
no code implementations • 26 Jun 2022 • Kailash Budhathoki, George Michailidis, Dominik Janzing
Existing methods of explainable AI and interpretable ML cannot explain change in the values of an output variable for a statistical unit in terms of the change in the input values and the change in the "mechanism" (the function transforming input to output).
2 code implementations • 14 Jun 2022 • Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing
We introduce DoWhy-GCM, an extension of the DoWhy Python library, that leverages graphical causal models.
no code implementations • 26 Feb 2021 • Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng
We describe a formal approach based on graphical causal models to identify the "root causes" of the change in the probability distribution of variables.
no code implementations • 6 Sep 2020 • Kailash Budhathoki, Mario Boley, Jilles Vreeken
Moreover, to discover reliable causal rules from a sample, we propose a conservative and consistent estimator of the causal effect, and derive an efficient and exact algorithm that maximises the estimator.
no code implementations • 1 Jul 2020 • Dominik Janzing, Patrick Blöbaum, Atalanti A. Mastakouri, Philipp M. Faller, Lenon Minorics, Kailash Budhathoki
We propose a notion of causal influence that describes the `intrinsic' part of the contribution of a node on a target node in a DAG.
no code implementations • 5 Dec 2019 • Dominik Janzing, Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum
We describe a formal approach to identify 'root causes' of outliers observed in $n$ variables $X_1,\dots, X_n$ in a scenario where the causal relation between the variables is a known directed acyclic graph (DAG).
no code implementations • 22 Feb 2017 • Kailash Budhathoki, Jilles Vreeken
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity.