Search Results for author:
Found 8 papers, 2 papers with code
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics
Representation learning methods that transform encoded data (e. g., diagnosis and drug codes) into continuous vector spaces (i. e., vector embeddings) are critical for the application of deep learning in healthcare.
Sample Constrained Treatment Effect Estimation
In particular, we study sample-constrained treatment effect estimation, where we must select a subset of $s \ll n$ individuals from the population to experiment on.
Efficient Intervention Design for Causal Discovery with Latents
We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process.
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
Our goal is to recover the directions of all causal or ancestral relations in $G$, via a minimum cost set of interventions.
How to Design Robust Algorithms using Noisy Comparison Oracle
Metric based comparison operations such as finding maximum, nearest and farthest neighbor are fundamental to studying various clustering techniques such as $k$-center clustering and agglomerative hierarchical clustering.
Collaborative Causal Discovery with Atomic Interventions
We introduce a new Collaborative Causal Discovery problem, through which we model a common scenario in which we have multiple independent entities each with their own causal graph, and the goal is to simultaneously learn all these causal graphs.
Continuous Treatment Effects with Surrogate Outcomes
In this paper, we study the role of surrogates in estimating continuous treatment effects and propose a doubly robust method to efficiently incorporate surrogates in the analysis, which uses both labeled and unlabeled data and does not suffer from the above selection bias problem.
Limits of Approximating the Median Treatment Effect
In the finite population setting containing $n$ individuals, with treatment and control values denoted by the potential outcome vectors $\mathbf{a}, \mathbf{b}$, much of the prior work focused on estimating median$(\mathbf{a}) -$ median$(\mathbf{b})$, where median($\mathbf x$) denotes the median value in the sorted ordering of all the values in vector $\mathbf x$.
