Search Results for author:
Found 12 papers, 10 papers with code
Causal Machine Learning for Cost-Effective Allocation of Development Aid
Then, we demonstrate our framework using real-world HIV data.
Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model
In this work, we develop a duration-dependent hidden Markov model.
Interpretable Off-Policy Learning via Hyperbox Search
Using a simulation study, we demonstrate that our algorithm outperforms state-of-the-art methods from interpretable off-policy learning in terms of regret.
Estimating average causal effects from patient trajectories
In medical practice, treatments are selected based on the expected causal effects on patient outcomes.
Estimating Conditional Average Treatment Effects with Missing Treatment Information
Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention.
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
While observational data is confounded, randomized data is unconfounded, but its sample size is usually too small to learn heterogeneous treatment effects.
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies
To this end, we develop the Deconfounding Temporal Autoencoder, a novel method that leverages observed noisy proxies to learn a hidden embedding that reflects the true hidden confounders.
Generalizing Off-Policy Learning under Sample Selection Bias
Since training data is often not representative of the target population, standard policy learning methods may yield policies that do not generalize target population.
Sequential Deconfounding for Causal Inference with Unobserved Confounders
In this paper, we develop the Sequential Deconfounder, a method that enables estimating individualized treatment effects over time in presence of unobserved confounders.
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units
In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs.
Estimating Average Treatment Effects via Orthogonal Regularization
In this paper, we propose a novel regularization framework for estimating average treatment effects that exploits unconfoundedness.
Estimating Treatment Effects via Orthogonal Regularization
Based on our regularization framework, we develop deep orthogonal networks for unconfounded treatments (DONUT) which learn outcomes that are orthogonal to the treatment assignment.
