Search Results for author:
Found 14 papers, 6 papers with code
The PetShop Dataset -- Finding Causes of Performance Issues across Microservices
Although the machine learning and systems research communities have proposed various techniques to tackle this problem, there is currently a lack of standardized datasets for quantitative benchmarking.
Beyond Single-Feature Importance with ICECREAM
Which set of features was responsible for a certain output of a machine learning model?
Toward Falsifying Causal Graphs Using a Permutation-Based Test
By comparing the number of inconsistencies with those on the surrogate baseline, we derive an interpretable metric that captures whether the DAG fits significantly better than random.
Interventional and Counterfactual Inference with Diffusion Models
We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph are available.
Thompson Sampling with Diffusion Generative Prior
In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems.
Manifold Restricted Interventional Shapley Values
Shapley values are model-agnostic methods for explaining model predictions.
Sequential Kernelized Independence Testing
Independence testing is a classical statistical problem that has been extensively studied in the batch setting when one fixes the sample size before collecting data.
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
We introduce DoWhy-GCM, an extension of the DoWhy Python library, that leverages graphical causal models.
Quantifying intrinsic causal contributions via structure preserving interventions
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.
Causal structure based root cause analysis of outliers
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).
Feature relevance quantification in explainable AI: A causal problem
We discuss promising recent contributions on quantifying feature relevance using Shapley values, where we observed some confusion on which probability distribution is the right one for dropped features.
Analysis of cause-effect inference by comparing regression errors
We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions.
Estimation of interventional effects of features on prediction
The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear.
Error Asymmetry in Causal and Anticausal Regression
It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated.
