Structural Agnostic Modeling: Adversarial Learning of Causal Graphs

A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries in the data, SAM aims at recovering full causal models from continuous observational data along a multivariate non-parametric setting. The approach is based on a game between $d$ players estimating each variable distribution conditionally to the others as a neural net, and an adversary aimed at discriminating the overall joint conditional distribution, and that of the original data. An original learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the end-to-end optimization of the graph structure and parameters through stochastic gradient descent. Besides the theoretical analysis of the approach in the large sample limit, SAM is extensively experimentally validated on synthetic and real data

Réseaux de Neurones Génératifs pour la Découverte de Mécanismes Causaux: Algorithmes et Applications

Causal discovery is of utmost importance for agents who must plan, reason anddecide based on observations; where mistaking correlation with causation mightlead to unwanted consequences. The goldstandard to discover causal relations is to perform experiments.However, experiments are in many cases expensive, unethical, or impossible torealize. In these situations, there is a need for observational causaldiscovery, that is, the estimation of causal relations from observations alone. Causal discovery in the observational data setting traditionally involves making significant assumptions on the data and on the underlying causal model.This thesis aims to alleviate some of the assumptions made on the causal models by exploiting the modularity and expressivenessof neural networks for causal discovery, leveraging both conditionalindependences and simplicity of the causal mechanisms through two algorithms.Extensive experimentson both simulated and real-world data and a throughout theoretical anaylsisprove the good performance and the soundness of the proposedapproaches.La découverte de relations causales est primordiale pour la planification,le raisonnement et la decision basée sur des données d'observations; confondrecorrelation et causalité ici peut mener à des conséquences indésirables. Laréférence pour la découverte de relations causales est d'effectuer desexpériences contrôlées. Mais dans la majorité des cas, ces expériences sontcoûteuses, immorales ou même impossibles à réaliser. Dans ces cas, il estnécessaire d'effectuer la découverte causale seulement sur des donnéesd'observations.Dans ce contexte de causalité observationnelle, retrouver des relations causalesintroduit traditionellement des hypothèses considérables sur les données et surle modèle causal sous-jacent.Cette thèse vise à relaxer certaines de ces hypothèses en exploitant lamodularité et l'expressivité des réseaux de neurones pour la causalité, enexploitant à la fois et indépendences conditionnelles et la simplicité desméchanismes causaux, à travers deux algorithmes. Des expériences extensives surdes données simulées et sur des données réelles ainsi qu'une analyse théoriqueapprofondie prouvent la cohérence et bonne performance des approches proposées

Causal Discovery Toolbox: Uncovering causal relationships in Python

International audienceThis paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The Cdt package implements an end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the causal relationships between variables. It includes algorithms from the 'Bnlearn' (Scutari, 2018) and 'Pcalg' (Kalisch et al., 2018) packages, together with algorithms for pairwise causal discovery such as ANM (Hoyer et al., 2009). Cdt is available under the MIT License at https://github.com/FenTechSolutions/CausalDiscoveryToolbox

Discriminant Learning Machines

International audienceThe cause-effect pair challenge has, for the first time, formulated the cause-effect problem as a learning problem in which a causation coefficient is trained from data. This can be thought of as a kind of meta learning. This chapter will present an overview of the contributions in this domain and state the advantages and limitations of the method as well as recent theoretical results (learning theory/mother distribution). This chapter will point to code from the winners of the cause-effect pair challenge