841 citations · 1.2k across the 34 of their papers we have counts for
15 papers · 1 filter
Intervention Generalization: A View from Factor Graph Models
Gecia Bravo-Hermsdorff, David S. Watson, Jialin Yu +2
One of the goals of causal inference is to generalize from past experiments and observational data to novel conditions. While it is in principle possible to eventually learn a mapp…
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…
Stochastic Causal Programming for Bounding Treatment Effects
Kirtan Padh, Jakob Zeitler, David Watson +3
Causal effect estimation is important for many tasks in the natural and social sciences. We design algorithms for the continuous partial identification problem: bounding the effect…
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…
Counterfactual Fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell +1
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In…
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Yin Cheng Ng, Pawel Chilinski, Ricardo Silva
Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences. We propose a scalable inference and learning algorith…
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…
Bayesian Inference in Cumulative Distribution Fields
Ricardo Silva
One approach for constructing copula functions is by multiplication. Given that products of cumulative distribution functions (CDFs) are also CDFs, an adjustment to this multiplica…
Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Ricardo Silva, Shohei Shimizu
Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions. If hidden common causes between treatment and outc…
Causal Inference through a Witness Protection Program
Ricardo Silva, Robin Evans
One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study, because on…
Flexible sampling of discrete data correlations without the marginal distributions
Alfredo Kalaitzis, Ricardo Silva
Learning the joint dependence of discrete variables is a fundamental problem in machine learning, with many applications including prediction, clustering and dimensionality reducti…
Latent Composite Likelihood Learning for the Structured Canonical Correlation Model
Ricardo Silva
Latent variable models are used to estimate variables of interest quantities which are observable only up to some measurement error. In many studies, such variables are known but n…