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Found 12 papers, 10 papers with code
Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
In this paper, we develop a novel approach to combine IV and observational data to enable reliable CATE estimation in the presence of unobserved confounding in the observational data and low compliance in the IV data, including no compliance for some subgroups.
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
We study the evaluation of a policy under best- and worst-case perturbations to a Markov decision process (MDP), using transition observations from the original MDP, whether they are generated under the same or a different policy.
Low-Rank MDPs with Continuous Action Spaces
Low-Rank Markov Decision Processes (MDPs) have recently emerged as a promising framework within the domain of reinforcement learning (RL), as they allow for provably approximately correct (PAC) learning guarantees while also incorporating ML algorithms for representation learning.
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
There has been recent progress on robust and efficient methods for estimating the conditional average treatment effect (CATE) function, but these methods often do not take into account the risk of hidden confounding, which could arbitrarily and unknowingly bias any causal estimate based on observational data.
Adaptive Bias Correction for Improved Subseasonal Forecasting
Subseasonal forecasting -- predicting temperature and precipitation 2 to 6 weeks ahead -- is critical for effective water allocation, wildfire management, and drought and flood mitigation.
Robust and Agnostic Learning of Conditional Distributional Treatment Effects
Our method is model-agnostic in that it can provide the best projection of CDTE onto the regression model class.
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
To streamline this process and accelerate future development, we introduce SubseasonalClimateUSA, a curated dataset for training and benchmarking subseasonal forecasting models in the United States.
Online Learning with Optimism and Delay
Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guarantees under delayed feedback.
Estimating the Long-Term Effects of Novel Treatments
Policy makers typically face the problem of wanting to estimate the long-term effects of novel treatments, while only having historical data of older treatment options.
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task).
Orthogonal Random Forest for Causal Inference
We provide a consistency rate and establish asymptotic normality for our estimator.
Flexible and Scalable Deep Learning with MMLSpark
In this work we detail a novel open source library, called MMLSpark, that combines the flexible deep learning library Cognitive Toolkit, with the distributed computing framework Apache Spark.
