9 citations · 23 across the 16 of their papers we have counts for
16 papers
GAR: Carbon-Aware Routing for LLM Inference via Constrained Optimization
Disha Sheshanarayana, Rajat Subhra Pal, Manjira Sinha +1
The growing deployment of large language models (LLMs) makes per-request routing essential for balancing response quality and computational cost across heterogeneous model pools. C…
Thinking in Latents: Adaptive Anchor Refinement for Implicit Reasoning in LLMs
Disha Sheshanarayana, Rajat Subhra Pal, Manjira Sinha +1
Token-level Chain-of-Thought (CoT) prompting has become a standard way to elicit multi-step reasoning in large language models (LLMs), especially for mathematical word problems. Ho…
Decoding Plastic Toxicity: An Intelligent Framework for Conflict-Aware Relational Metapath Extraction from Scientific Abstracts
Sudeshna Jana, Manjira Sinha, Tirthankar Dasgupta
The widespread use of plastics and their persistence in the environment have led to the accumulation of micro- and nano-plastics across air, water, and soil, posing serious health…
StayLTC: A Cost-Effective Multimodal Framework for Hospital Length of Stay Forecasting
Sudeshna Jana, Manjira Sinha, Tirthankar Dasgupta
Accurate prediction of Length of Stay (LOS) in hospitals is crucial for improving healthcare services, resource management, and cost efficiency. This paper presents StayLTC, a mult…
Mapping Patient Trajectories: Understanding and Visualizing Sepsis Prognostic Pathways from Patients Clinical Narratives
Sudeshna Jana, Tirthankar Dasgupta, Lipika Dey
In recent years, healthcare professionals are increasingly emphasizing on personalized and evidence-based patient care through the exploration of prognostic pathways. To study this…
Leveraging the Fisher randomization test using confidence distributions: inference, combination and fusion learning
Xiaokang Luo, Tirthankar Dasgupta, Minge Xie +1
The flexibility and wide applicability of the Fisher randomization test (FRT) makes it an attractive tool for assessment of causal effects of interventions from modern-day randomiz…
An Email Experiment to Identify the Effect of Racial Discrimination on Access to Lawyers: A Statistical Approach
Brian Libgober, Tirthankar Dasgupta
We consider the problem of conducting an experiment to study the prevalence of racial bias against individuals seeking legal assistance, in particular whether lawyers use clues abo…
Causal Inference from Possibly Unbalanced Split-Plot Designs: A Randomization-based Perspective
Rahul Mukerjee, Tirthankar Dasgupta
Split-plot designs find wide applicability in multifactor experiments with randomization restrictions. Practical considerations often warrant the use of unbalanced designs. This pa…
Sampling-based randomized designs for causal inference under the potential outcomes framework
Zach Branson, Tirthankar Dasgupta
We establish the inferential properties of the mean-difference estimator for the average treatment effect in randomized experiments where each unit in a population is randomized to…
A randomization-based perspective of analysis of variance: a test statistic robust to treatment effect heterogeneity
Peng Ding, Tirthankar Dasgupta
Fisher randomization tests for Neyman's null hypothesis of no average treatment effects are considered in a finite population setting associated with completely randomized experime…
Causal Inference in Rebuilding and Extending the Recondite Bridge between Finite Population Sampling and Experimental Design
Rahul Mukerjee, Tirthankar Dasgupta, Donald B. Rubin
This article considers causal inference for treatment contrasts from a randomized experiment using potential outcomes in a finite population setting. Adopting a Neymanian repeated…
Improving Covariate Balance in 2^K Factorial Designs via Rerandomization
Zach Branson, Tirthankar Dasgupta, Donald B. Rubin
Factorial designs are widely used in agriculture, engineering, and the social sciences to study the causal effects of several factors simultaneously on a response. The objective of…
Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds
Jiannan Lu, Peng Ding, Tirthankar Dasgupta
Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we c…
Construction of alternative hypotheses for randomization tests with ordinal outcomes
Jiannan Lu, Peng Ding, Tirthankar Dasgupta
For ordinal outcomes, we construct sequences of alternative hypotheses in increasing departures from the sharp null hypothesis of zero treatment effect on each experimental unit, t…
A Potential Tale of Two by Two Tables from Completely Randomized Experiments
Peng Ding, Tirthankar Dasgupta
Causal inference in completely randomized treatment-control studies with binary outcomes is discussed from Fisherian, Neymanian and Bayesian perspectives, using the potential outco…
Causal inference from factorial designs using the potential outcomes model
Tirthankar Dasgupta, Natesh S. Pillai, Donald B. Rubin
A framework for causal inference from two-level factorial designs is proposed. The framework utilizes the concept of potential outcomes that lies at the center stage of causal infe…