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Found 9 papers, 4 papers with code
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection
Motivated by the success of SSMs, we present MambaMixer, a new architecture with data-dependent weights that uses a dual selection mechanism across tokens and channels, called Selective Token and Channel Mixer.
Stable Estimation of Survival Causal Effects
Our experiments on synthetic and semi-synthetic data demonstrate that our method has competitive bias and smaller variance than debiased machine learning approaches.
A Fast Bootstrap Algorithm for Causal Inference with Large Data
The bag of little bootstraps has been proposed in non-causal settings for large data but has not yet been applied to evaluate the properties of estimators of causal effects.
Deep survival analysis with longitudinal X-rays for COVID-19
Time-to-event analysis is an important statistical tool for allocating clinical resources such as ICU beds.
Kernel Optimal Orthogonality Weighting: A Balancing Approach to Estimating Effects of Continuous Treatments
In this paper, we propose Kernel Optimal Orthogonality Weighting (KOOW), a convex optimization-based method, for estimating the effects of continuous treatments.
Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching
In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects.
More robust estimation of sample average treatment effects using Kernel Optimal Matching in an observational study of spine surgical interventions
Inverse probability of treatment weighting (IPTW), which has been used to estimate sample average treatment effects (SATE) using observational data, tenuously relies on the positivity assumption and the correct specification of the treatment assignment model, both of which are problematic assumptions in many observational studies.
Methodology stat.ML, stat.ME, stat.AP
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
In addition, it is sub-differentiable such that it can be used for learning, unlike the SWITCH estimator.
Optimal Balancing of Time-Dependent Confounders for Marginal Structural Models
Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression.
