322 citations · 915 across the 42 of their papers we have counts for
6 papers · 1 filter
Graph Distance Based on Cause-Effect Estimands with Latents
Zhufeng Li, Niki Kilbertus
Causal discovery aims to recover graphs that represent causal relations among given variables from observations, and new methods are constantly being proposed. Increasingly, the co…
Predicting symbolic ODEs from multiple trajectories
Yakup Emre Şahin, Niki Kilbertus, Sören Becker
We introduce MIO, a transformer-based model for inferring symbolic ordinary differential equations (ODEs) from multiple observed trajectories of a dynamical system. By combining mu…
An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
Uzair Akbar, Niki Kilbertus, Hao Shen +2
The technique of data augmentation (DA) is often used in machine learning for regularization purposes to better generalize under i.i.d. settings. In this work, we present a unifyin…
Identifiability Challenges in Sparse Linear Ordinary Differential Equations
Cecilia Casolo, Sören Becker, Niki Kilbertus
Dynamical systems modeling is a core pillar of scientific inquiry across natural and life sciences. Increasingly, dynamical system models are learned from data, rendering identifia…
An Asymmetric Independence Model for Causal Discovery on Path Spaces
Georg Manten, Cecilia Casolo, Søren Wengel Mogensen +1
We develop the theory linking 'E-separation' in directed mixed graphs (DMGs) with conditional independence relations among coordinate processes in stochastic differential equations…
Your Assumed DAG is Wrong and Here's How To Deal With It
Kirtan Padh, Zhufeng Li, Cecilia Casolo +1
Assuming a directed acyclic graph (DAG) that represents prior knowledge of causal relationships between variables is a common starting point for cause-effect estimation. Existing l…