The van der Schaar Lab is delighted to announced that Prof. Mihaela van der Schaar will be giving a keynote talk at the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), one of the most prominent gatherings of researchers at the intersection of artificial intelligence, machine learning, statistics, and related areas.
Mihaela’s AISTATS 2022 keynote will take place on March 30 at 10:30-11:30 BST (other time zones here). Further details be shared over the coming weeks as they are made available by the conference’s organizers. In the meantime, information about AISTATS 2022 (scheduled for March 28-30, 2022) can be found on the event’s official website.
Additionally, one of the lab’s papers has also been selected for publication at AISTATS 2022.
Identifiable Energy-based Representations:
An Application to Estimating Heterogeneous Causal Effects
Abstract and URL
Conditional average treatment effects (CATEs) allow us to understand the effect heterogeneity across a large population of individuals.
However, typical CATE learners assume all confounding variables are measured in order for the CATE to be identifiable. This requirement can be satisfied by collecting many variables, at the expense of increased sample complexity for estimating CATEs.
To combat this, we propose an energy-based model (EBM) that learns a low-dimensional representation of the variables by employing a noise contrastive loss function. With our EBM we introduce a preprocessing step that alleviates the dimensionality curse for any existing learner developed for estimating CATEs. We prove that our EBM keeps the representations partially identifiable up to some universal constant, as well as having universal approximation capability. These properties enable the representations to converge and keep the CATE estimates consistent.
Experiments demonstrate the convergence of the representations, as well as show that estimating CATEs on our representations performs better than on the variables or the representations obtained through other dimensionality reduction methods.
AISTATS is an interdisciplinary gathering of researchers at the intersection of computer science, artificial intelligence, machine learning, statistics, and related areas.
Since its inception in 1985, the primary goal of AISTATS has been to broaden research in these fields by promoting the exchange of ideas among them. We encourage the submission of all papers which are in keeping with this objective at AISTATS.
For a full list of the van der Schaar Lab’s publications at the top AI and ML conferences, click here.