1 citations · 2 across the 9 of their papers we have counts for
9 papers
Falsifying Causal Graphs With Outlier Events
William Roy Orchard, Philipp M. Faller, Dominik Janzing
True causal relationships are rarely known, and inferring causal graphs from data is hard. A fundamental challenge is how to assess whether a given causal graph is good in the abse…
From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?
Sujai Hiremath, Dominik Janzing, Philipp Faller +4
Causal discovery algorithms often perform poorly with limited samples. While integrating expert knowledge (including from LLMs) as constraints promises to improve performance, guar…
On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models
Philipp M. Faller, Dominik Janzing
Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variables in a dataset. These tests, how…
Cross-validating causal discovery via Leave-One-Variable-Out
Daniela Schkoda, Philipp Faller, Patrick Blöbaum +1
We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variable pair excluded during learning the ca…
Score matching through the roof: linear, nonlinear, and latent variables causal discovery
Francesco Montagna, Philipp M. Faller, Patrick Bloebaum +2
Causal discovery from observational data holds great promise, but existing methods rely on strong assumptions about the underlying causal structure, often requiring full observabil…
Self-Compatibility: Evaluating Causal Discovery without Ground Truth
Philipp M. Faller, Leena Chennuru Vankadara, Atalanti A. Mastakouri +2
As causal ground truth is incredibly rare, causal discovery algorithms are commonly only evaluated on simulated data. This is concerning, given that simulations reflect preconcepti…
Reinterpreting causal discovery as the task of predicting unobserved joint statistics
Dominik Janzing, Philipp M. Faller, Leena Chennuru Vankadara
If denote sets of random variables, two different data sources may contain samples from and , respectively. We argue that causal discovery can help infer…
Causal Forecasting:Generalization Bounds for Autoregressive Models
Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt +3
Despite the increasing relevance of forecasting methods, causal implications of these algorithms remain largely unexplored. This is concerning considering that, even under simplify…
Quantifying intrinsic causal contributions via structure preserving interventions
Dominik Janzing, Patrick Blöbaum, Atalanti A. Mastakouri +3
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. By recursively writing each node as a function…