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Johannes Textor

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12 papers
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12

UAI Conference 2025 Conference Paper

Expert-In-The-Loop Causal Discovery: Iterative Model Refinement Using Expert Knowledge

  • Ankur Ankan
  • Johannes Textor

Many researchers construct directed acyclic graph (DAG) models manually based on domain knowledge. Although numerous causal discovery algorithms were developed to automatically learn DAGs and other causal models from data, these remain challenging to use due to their tendency to produce results that contradict domain knowledge, among other issues. Here we propose a hybrid, iterative structure learning approach that combines domain knowledge with data-driven insights to assist researchers in constructing DAGs. Our method leverages conditional independence testing to iteratively identify variable pairs where an edge is either missing or superfluous. Based on this information, we can choose to add missing edges with appropriate orientation based on domain knowledge or remove unnecessary ones. We also give a method to rank these missing edges based on their impact on the overall model fit. In a simulation study, we find that this iterative approach to leverage domain knowledge already starts outperforming purely data-driven structure learning if the orientation of new edge is correctly determined in at least two out of three cases. We present a proof-of-concept implementation using a large language model as a domain expert and a graphical user interface designed to assist human experts with DAG construction.

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JMLR Journal 2024 Journal Article

pgmpy: A Python Toolkit for Bayesian Networks

  • Ankur Ankan
  • Johannes Textor

Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for structure learning, parameter estimation, approximate and exact inference, causal inference, and simulations. These implementations focus on modularity and easy extensibility to allow users to quickly modify/add to existing algorithms, or to implement new algorithms for different use cases. pgmpy is released under the MIT License; the source code is available at: https://github.com/pgmpy/pgmpy, and the documentation at: https://pgmpy.org. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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AAAI Conference 2023 Conference Paper

A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data

  • Ankur Ankan
  • Johannes Textor

Conditional independence (CI) tests underlie many approaches to model testing and structure learning in causal inference. Most existing CI tests for categorical and ordinal data stratify the sample by the conditioning variables, perform simple independence tests in each stratum, and combine the results. Unfortunately, the statistical power of this approach degrades rapidly as the number of conditioning variables increases. Here we propose a simple unified CI test for ordinal and categorical data that maintains reasonable calibration and power in high dimensions. We show that our test outperforms existing baselines in model testing and structure learning for dense directed graphical models while being comparable for sparse models. Our approach could be attractive for causal model testing because it is easy to implement, can be used with non-parametric or parametric probability models, has the symmetry property, and has reasonable computational requirements.

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JMLR Journal 2018 Journal Article

Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs

  • Emilija Perković
  • Johannes Textor
  • Markus Kalisch
  • Marloes H. Maathuis

We present a graphical criterion for covariate adjustment that is sound and complete for four different classes of causal graphical models: directed acyclic graphs (DAGs), maximal ancestral graphs (MAGs), completed partially directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our criterion unifies covariate adjustment for a large set of graph classes. Moreover, we define an explicit set that satisfies our criterion, if there is any set that satisfies our criterion. We also give efficient algorithms for constructing all sets that fulfill our criterion, implemented in the R package dagitty. Finally, we discuss the relationship between our criterion and other criteria for adjustment, and we provide new soundness and completeness proofs for the adjustment criterion for DAGs. [abs] [ pdf ][ bib ] &copy JMLR 2018. ( edit, beta )

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UAI Conference 2015 Conference Paper

A Complete Generalized Adjustment Criterion

  • Emilija Perkovic
  • Johannes Textor
  • Markus Kalisch
  • Marloes H. Maathuis

Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify valid covariates for adjustment from graphical causal models. These criteria can handle multiple causes, latent confounding, or partial knowledge of the causal structure; however, their diversity is confusing and some of them are only sufficient, but not necessary. In this paper, we present a criterion that is necessary and sufficient for four different classes of graphical causal models: directed acyclic graphs (DAGs), maximum ancestral graphs (MAGs), completed partially directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our criterion subsumes the existing ones and in this way unifies adjustment set construction for a large set of graph classes.

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IJCAI Conference 2015 Conference Paper

Efficiently Finding Conditional Instruments for Causal Inference

  • Benito van der Zander
  • Johannes Textor
  • Maciej Liskiewicz

Instrumental variables (IVs) are widely used to identify causal effects. For this purpose IVs have to be exogenous, i. e. , causally unrelated to all variables in the model except the explanatory variable X. It can be hard to find such variables. A generalized IV method has been proposed that only requires exogeneity conditional on a set of covariates. This leads to a wider choice of potential IVs, but is rarely used yet. Here we address two issues with conditional IVs. First, they are conceptually rather distant to standard IVs; even variables that are independent of X could qualify as conditional IVs. We propose a new concept called ancestral IV, which interpolates between the two existing notions. Second, so far only exponential-time algorithms are known to find conditional IVs in a given causal diagram. Indeed, we prove that this problem is NP-hard. Nevertheless, we show that whenever a conditional IV exists, so does an ancestral IV, and ancestral IVs can be found in polynomial time. Together this implies a complete and constructive solution to causal effect identification using IVs in linear causal models.

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UAI Conference 2015 Conference Paper

Learning from Pairwise Marginal Independencies

  • Johannes Textor
  • Alexander Idelberger
  • Maciej Liskiewicz

We consider graphs that represent pairwise marginal independencies amongst a set of variables (for instance, the zero entries of a covariance matrix for normal data). We characterize the directed acyclic graphs (DAGs) that faithfully explain a given set of independencies, and derive algorithms to efficiently enumerate such structures. Our results map out the space of faithful causal models for a given set of pairwise marginal independence relations. This allows us to show the extent to which causal inference is possible without using conditional independence tests.

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UAI Conference 2014 Conference Paper

Constructing Separators and Adjustment Sets in Ancestral Graphs

  • Benito van der Zander
  • Maciej Liskiewicz
  • Johannes Textor

Ancestral graphs (AGs) are graphical causal models that can represent uncertainty about the presence of latent confounders, and can be inferred from data. Here, we present an algorithmic framework for efficiently testing, constructing, and enumerating m-separators in AGs. Moreover, we present a new constructive criterion for covariate adjustment in directed acyclic graphs (DAGs) and maximal ancestral graphs (MAGs) that characterizes adjustment sets as mseparators in a subgraph. Jointly, these results allow to find all adjustment sets that can identify a desired causal effect with multivariate exposures and outcomes in the presence of latent confounding. Our results generalize and improve upon several existing solutions for special cases of these problems.

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