PostDoc, ETH Zurich
Nonparametric Causal Representation Learning from Multiple Environments
A key obstacle to more widespread use of causal models in AI is requiring the relevant variables to be specified a priori. Yet, the causal relations of interest often do not occur at the level of raw observations such as pixels, but instead play out among abstract high-level latent concepts. At the same time, ML has proven successful at automatically extracting useful and compact representations of such complex data. Causal representation learning (CRL) aims to combine the core strengths of ML and causality by learning representations in the form of latent variables endowed with causal structure and interventional semantics.
In this talk, I will present two recent identifiability studies [1,2] for CRL from multiple domains or environments arising from interventions on the latent causal variables. In particular, we focus on the nonparametric case in which both the latent causal model and the mixing function are completely unconstrained. First, we introduce Causal Component Analysis (CauCA) [1], a generalization of independent component analysis (ICA), in which the causal graph is known but non-trivial. As a special case of CRL, CauCA provides insights into necessary assumptions; it also yields novel identifiability results for ICA. We then study the full CRL setting [2] with unknown graph and intervention targets and prove identifiability subject to a suitable notion of genericity or given coupled interventions.
[1] L Wendong, A Kekić, J von Kügelgen, S Buchholz, M Besserve, L Gresele, B Schölkopf. Causal Component Analysis. In: Advances in Neural Information Processing Systems, 2023.
[2] J von Kügelgen, M Besserve, L Wendong, L Gresele, A Kekić, E Bareinboim, DM Blei, B Schölkopf. Nonparametric Identifiability of Causal Representations from Unknown Interventions. In: Advances in Neural Information Processing Systems, 2023.