9 citations · 28 across the 10 of their papers we have counts for
7 papers · 1 filter
The Causal Marginal Polytope for Bounding Treatment Effects
Jakob Zeitler, Ricardo Silva
Due to unmeasured confounding, it is often not possible to identify causal effects from a postulated model. Nevertheless, we can ask for partial identification, which usually boils…
Neural Likelihoods via Cumulative Distribution Functions
Pawel Chilinski, Ricardo Silva
We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs). By the applicatio…
Causal Interventions for Fairness
Matt J. Kusner, Chris Russell, Joshua R. Loftus +1
Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help pr…
Alpha-Beta Divergence For Variational Inference
Jean-Baptiste Regli, Ricardo Silva
This paper introduces a variational approximation framework using direct optimization of what is known as the {\it scale invariant Alpha-Beta divergence} (sAB divergence). This new…
A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
Yin Cheng Ng, Ricardo Silva
We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks, in contrast to existing models which are dense. The mod…
Observational-Interventional Priors for Dose-Response Learning
Ricardo Silva
Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment lev…