Stefan Bauer

Fabian J. Theis

2025-03-25

ArXiv (preprint)

A scalable gene network model of regulatory dynamics in single cells

Paul Bertin

Joseph D Viviano

Alejandro Tejada-Lapuerta

Weixu Wang

Fabian J. Theis

2025-03-25

ArXiv (preprint)

Learning Decision Trees as Amortized Structure Inference

Mohammed Mahfoud

Ghait Boukachab

Michał Koziarski

Alex Hernandez-Garcia

Nikolay Malkin

Building predictive models for tabular data presents fundamental challenges, notably in scaling consistently, i.e., more resources translati… (see more)ng to better performance, and generalizing systematically beyond the training data distribution. Designing decision tree models remains especially challenging given the intractably large search space, and most existing methods rely on greedy heuristics, while deep learning inductive biases expect a temporal or spatial structure not naturally present in tabular data. We propose a hybrid amortized structure inference approach to learn predictive decision tree ensembles given data, formulating decision tree construction as a sequential planning problem. We train a deep reinforcement learning (GFlowNet) policy to solve this problem, yielding a generative model that samples decision trees from the Bayesian posterior. We show that our approach, DT-GFN, outperforms state-of-the-art decision tree and deep learning methods on standard classification benchmarks derived from real-world data, robustness to distribution shifts, and anomaly detection, all while yielding interpretable models with shorter description lengths. Samples from the trained DT-GFN model can be ensembled to construct a random forest, and we further show that the performance of scales consistently in ensemble size, yielding ensembles of predictors that continue to generalize systematically.

2025-03-10

ArXiv (preprint)

Learning Decision Trees as Amortized Structure Inference

Mohammed Mahfoud

Ghait Boukachab

Michał Koziarski

Alex Hernandez-Garcia

Nikolay Malkin

2025-03-10

ArXiv (preprint)

Learning Decision Trees as Amortized Structure Inference

Mohammed Mahfoud

Ghait Boukachab

Michał Koziarski

Alex Hernandez-Garcia

Nikolay Malkin

2025-03-05

ICLR.cc/2025/Workshop/FPI (poster)

Causal machine learning for single-cell genomics

Alejandro Tejada-Lapuerta

Paul Bertin

Hananeh Aliee

Fabian J. Theis

2023-10-23

ArXiv (preprint)

Neural Causal Structure Discovery from Interventions

Nan Rosemary Ke

Bernhard Schölkopf

Michael Curtis Mozer

Chris Pal

Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (see more) However, there are theoretical limitations on the identifiability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository.

2023-09-10

TMLR (accepted)

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inf… (see more)erence. Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. To address this gap, we evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets, on the downstream task of treatment effect estimation. Through the implementation of a distribution-level evaluation, we offer valuable and unique insights into the efficacy of these causal discovery methods for treatment effect estimation, considering both synthetic and real-world scenarios, as well as low-data scenarios. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes, while some tend to learn many low-probability modes which impacts the (unrelaxed) recall and precision.

2023-07-11

ArXiv (preprint)

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

Neural Causal Structure Discovery from Interventions

Nan Rosemary Ke

Bernhard Schölkopf

Michael Curtis Mozer

Chris Pal

Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (see more) However, there are theoretical limitations on the identiﬁability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository

2023-01-01

Trans. Mach. Learn. Res. (published)

dblp.uni-trier.de

Learning Latent Structural Causal Models

Jithendaraa Subramanian

Yashas Annadani

Ivaxi Sheth

Nan Rosemary Ke

Tristan Deleu

Derek Nowrouzezahrai

Samira Ebrahimi Kahou

Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better e… (see more)xplanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given. However, in machine learning tasks, one often operates on low-level data like image pixels or high-dimensional vectors. In such settings, the entire Structural Causal Model (SCM) -- structure, parameters, \textit{and} high-level causal variables -- is unobserved and needs to be learnt from low-level data. We treat this problem as Bayesian inference of the latent SCM, given low-level data. For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions. Experiments are performed on synthetic datasets and a causally generated image dataset to demonstrate the efficacy of our approach. We also perform image generation from unseen interventions, thereby verifying out of distribution generalization for the proposed causal model.

2022-10-24

ArXiv (preprint)