Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders

We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders. To overcome the problem of hidden confounding, we consider the setting where we have access to not only the observational data but also sets of single-variable interventions in which each of the treatment variables is intervened on separately. We prove identifiability under the assumption that the data is generated from a nonlinear continuous structural causal model with additive Gaussian noise. In addition, we propose a simple parameter estimation method by pooling all the data from different regimes and jointly maximizing the combined likelihood. We also conduct comprehensive experiments to verify the identifiability result as well as to compare the performance of our approach against a baseline on both synthetic and real-world data.Comment: Accepted to The Conference on Uncertainty in Artificial Intelligence (UAI) 202

backShift: Learning causal cyclic graphs from unknown shift interventions

We propose a simple method to learn linear causal cyclic models in the presence of latent variables. The method relies on equilibrium data of the model recorded under a specific kind of interventions ("shift interventions"). The location and strength of these interventions do not have to be known and can be estimated from the data. Our method, called backShift, only uses second moments of the data and performs simple joint matrix diagonalization, applied to differences between covariance matrices. We give a sufficient and necessary condition for identifiability of the system, which is fulfilled almost surely under some quite general assumptions if and only if there are at least three distinct experimental settings, one of which can be pure observational data. We demonstrate the performance on some simulated data and applications in flow cytometry and financial time series. The code is made available as R-package backShift

Causation as a high-level affair

The causal exclusion argument supports the notion that causation should be thought of as a purely low-level affair. Here we argue instead in favour of high-level causation as a natural and meaningful notion that may even be more useful than causation at more fundamental physical levels. Our argument is framed in terms of a broadly interventionist conception of causation. Its essence is that causal relations at an appropriately high level can in a certain sense be less sensitive than those at a fundamental, microscopic level. This means that in settings where causal relations at the (micro-) physical level are not considered in the context of some suitable macro-level interpretation, statements concerning the low-level relations may be highly sensitive with respect to changes in background conditions. Using an example of accelerator experiments in particle physics, we consider what it means to characterize extremely sensitive low-level events as causal