6 accepted papers
Causal discovery methods commonly assume that all data is independently and identically distributed (i.i.d.) and that there are no unmeasured confounding variables. In practice, neither is likely to hold, and detecting confounding in non-i.i.d. settings poses a significant challenge. Motivated by th…
Causal sufficiency is a cornerstone assumption in causal discovery. It is, however, both unlikely to hold in practice as well as unverifiable. When it does not hold, existing methods struggle to return meaningful results. In this paper, we show how to discover the causal network over both observed a…
Access to a representative sample from the population is an assumption that underpins all of machine learning. Selection effects can cause observations to instead come from a subpopulation, by which our inferences may be subject to bias. It is therefore important to know whether or not a sample is…
In many scientific applications, we observe a system in different conditions in which its components may change, rather than in isolation. In our work, we are interested in explaining the generating process of such a multi-context system using a finite mixture of causal mechanisms. Recent work shows…
Causal discovery, the task of discovering the causal graph over a set of observed variables $X_1,\ldots,X_m$, is a challenging problem. One of the cornerstone assumptions is that of causal sufficiency: that *all* common causes of *all* measured variables have been observed. When it does not hold, ca…
In critical applications, causal models are the prime choice for their trustworthiness and explainability. If data is inherently distributed and privacy-sensitive, federated learning allows for collaboratively training a joint model. Existing approaches for federated causal discovery share locally d…