no code implementations • 29 Mar 2024 • Ali Behrouz, Michele Santacatterina, Ramin Zabih
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.
no code implementations • 1 Oct 2023 • Khiem Pham, David A. Hirshberg, Phuong-Mai Huynh-Pham, Michele Santacatterina, Ser-Nam Lim, Ramin Zabih
We propose an empirically stable and asymptotically efficient covariate-balancing approach to the problem of estimating survival causal effects in data with conditionally-independent censoring.
1 code implementation • 6 Feb 2023 • Matthew Kosko, Lin Wang, Michele Santacatterina
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.
no code implementations • ICCV 2021 • Michelle Shu, Richard Strong Bowen, Charles Herrmann, Gengmo Qi, Michele Santacatterina, Ramin Zabih
Time-to-event analysis is an important statistical tool for allocating clinical resources such as ICU beds.
no code implementations • 26 Oct 2019 • Nathan Kallus, Michele Santacatterina
In this paper, we propose Kernel Optimal Orthogonality Weighting (KOOW), a convex optimization-based method, for estimating the effects of continuous treatments.
1 code implementation • 13 Aug 2019 • Nathan Kallus, Michele Santacatterina
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.
1 code implementation • 10 Nov 2018 • Nathan Kallus, Brenton Pennicooke, Michele Santacatterina
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
no code implementations • 6 Nov 2018 • Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims
In addition, it is sub-differentiable such that it can be used for learning, unlike the SWITCH estimator.
1 code implementation • 4 Jun 2018 • Nathan Kallus, Michele Santacatterina
Marginal structural models (MSMs) estimate the causal effect of a time-varying treatment in the presence of time-dependent confounding via weighted regression.