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Found 8 papers, 5 papers with code
Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes
Weighting is a general and often-used method for statistical adjustment.
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
We propose a simple and general framework for nonparametric estimation of heterogeneous treatment effects under fairness constraints.
Doubly Robust Counterfactual Classification
We study counterfactual classification as a new tool for decision-making under hypothetical (contrary to fact) scenarios.
PLLay: Efficient Topological Layer based on Persistent Landscapes
We propose PLLay, a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological features of the input data structure.
PLLay: Efficient Topological Layer based on Persistence Landscapes
We propose PLLay, a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological features of the input data structure.
Incremental Intervention Effects in Studies with Dropout and Many Timepoints
Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size.
Time Series Featurization via Topological Data Analysis
We develop a novel algorithm for feature extraction in time series data by leveraging tools from topological data analysis.
Causal effects based on distributional distances
In this paper we develop a framework for characterizing causal effects via distributional distances.
