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Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
Armin Kekić, Sergio Hernan Garrido Mejia, Bernhard Schölkopf
Estimating causal effects of joint interventions on multiple variables is crucial in many domains, but obtaining data from such simultaneous interventions can be challenging. Our s…
Independent Mechanism Analysis and the Manifold Hypothesis
Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen +2
Independent Mechanism Analysis (IMA) seeks to address non-identifiability in nonlinear Independent Component Analysis (ICA) by assuming that the Jacobian of the mixing function has…
Targeted Reduction of Causal Models
Armin Kekić, Bernhard Schölkopf, Michel Besserve
Why does a phenomenon occur? Addressing this question is central to most scientific inquiries and often relies on simulations of scientific models. As models become more intricate,…
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk +2
Selecting hyperparameters in deep learning greatly impacts its effectiveness but requires manual effort and expertise. Recent works show that Bayesian model selection with Laplace…
Nonparametric Identifiability of Causal Representations from Unknown Interventions
Julius von Kügelgen, Michel Besserve, Liang Wendong +5
We study causal representation learning, the task of inferring latent causal variables and their causal relations from high-dimensional mixtures of the variables. Prior work relies…
Causal Component Analysis
Liang Wendong, Armin Kekić, Julius von Kügelgen +4
Independent Component Analysis (ICA) aims to recover independent latent variables from observed mixtures thereof. Causal Representation Learning (CRL) aims instead to infer causall…