arXiv subjects

Pradipta Sarkar

Publications and source records attributed to Pradipta Sarkar.

3 recordsLinked to original sources

arXiv2026

Causal Inference of Ordinal Outcomes: A Bayesian Solution

Randomized experiments with ordinal outcomes are common in many scientific applications, but conventional causal estimands such as the average treatment effect are difficult to interpret because ordinal categories lack meaningful numerical spacing. We develop a Bayesian latent variable framework for drawing coherent super population and finite population inference on two interpretable causal estimands that quantify the probabilities that treatment is beneficial and strictly beneficial. By modeling the joint distribution of potential outcomes through an ordered probit model, the proposed approach overcomes the identifiability limitations of existing methods and yields substantially sharper inference than nonparametric bounds. We also investigate the impact of the unknown association between potential outcomes and propose a sensitivity analysis to assess its influence. Simulation studies and an application to a randomized experiment on human scalp health demonstrate that the method provides precise and practically relevant assessments of treatment effectiveness.

stat.ME↗

arXiv2025

Detecting and Preventing Data Poisoning Attacks on AI Models

This paper investigates the critical issue of data poisoning attacks on AI models, a growing concern in the ever-evolving landscape of artificial intelligence and cybersecurity. As advanced technology systems become increasingly prevalent across various sectors, the need for robust defence mechanisms against adversarial attacks becomes paramount. The study aims to develop and evaluate novel techniques for detecting and preventing data poisoning attacks, focusing on both theoretical frameworks and practical applications. Through a comprehensive literature review, experimental validation using the CIFAR-10 and Insurance Claims datasets, and the development of innovative algorithms, this paper seeks to enhance the resilience of AI models against malicious data manipulation. The study explores various methods, including anomaly detection, robust optimization strategies, and ensemble learning, to identify and mitigate the effects of poisoned data during model training. Experimental results indicate that data poisoning significantly degrades model performance, reducing classification accuracy by up to 27% in image recognition tasks (CIFAR-10) and 22% in fraud detection models (Insurance Claims dataset). The proposed defence mechanisms, including statistical anomaly detection and adversarial training, successfully mitigated poisoning effects, improving model robustness and restoring accuracy levels by an average of 15-20%. The findings further demonstrate that ensemble learning techniques provide an additional layer of resilience, reducing false positives and false negatives caused by adversarial data injections.

cs.CR↗

arXiv2017

A conditional randomization test to account for covariate imbalance in randomized experiments

We consider the conditional randomization test as a way to account for covariate imbalance in randomized experiments. The test accounts for covariate imbalance by comparing the observed test statistic to the null distribution of the test statistic conditional on the observed covariate imbalance. We prove that the conditional randomization test has the correct significance level and introduce original notation to describe covariate balance more formally. Through simulation, we verify that conditional randomization tests behave like more traditional forms of covariate adjustmet but have the added benefit of having the correct conditional significance level. Finally, we apply the approach to a randomized product marketing experiment where covariate information was collected after randomization.

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