Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …

Metal halide perovskites (MHPs) have catapulted to the forefront of energy research due to
the unique combination of high device performance, low materials cost, and facile solution …

How should we intervene on an unknown structural equation model to maximize a
downstream variable of interest? This setting, also known as causal Bayesian optimization …

We study the problem of performing a sequence of optimal interventions in a dynamic causal
system where both the target variable of interest, and the inputs, evolve over time. This …

We propose a new causal inference framework to learn causal effects from multiple,
decentralized data sources in a federated setting. We introduce an adaptive transfer …

We study the problem of globally optimizing the causal effect on a target variable of an
unknown causal graph in which interventions can be performed. This problem arises in …

While causal models are becoming one of the mainstays of machine learning, the problem
of uncertainty quantification in causal inference remains challenging. In this paper, we study …

We introduce a Bayesian multi-task Gaussian process model for estimating treatment effects
from panel data, where an intervention outside the observer's control influences a subset of …

This work considers the problem of transferring causal knowledge between tasks for
Individual Treatment Effect (ITE) estimation. To this end, we theoretically assess the …

We introduce a new statistical causal inference method to estimate individual-level optimal
causal intervention, that is, to which value we should set the value of a certain variable of an …