21 citations · 24 across the 10 of their papers we have counts for
12 papers
Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks
Ashutosh Srivastava, Lokesh Nagalapatti, Gautam Jajoo +3
Recent claims of strong performance by Large Language Models (LLMs) on causal discovery are undermined by a key flaw: many evaluations rely on benchmarks likely included in pretrai…
From Search To Sampling: Generative Models For Robust Algorithmic Recourse
Prateek Garg, Lokesh Nagalapatti, Sunita Sarawagi
Algorithmic Recourse provides recommendations to individuals who are adversely impacted by automated model decisions, on how to alter their profiles to achieve a favorable outcome.…
Robust Root Cause Diagnosis using In-Distribution Interventions
Lokesh Nagalapatti, Ashutosh Srivastava, Sunita Sarawagi +1
Diagnosing the root cause of an anomaly in a complex interconnected system is a pressing problem in today's cloud services and industrial operations. We propose In-Distribution Int…
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE
Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh +1
Treatment effect estimation involves assessing the impact of different treatments on individual outcomes. Current methods estimate Conditional Average Treatment Effect (CATE) using…
Tab-Shapley: Identifying Top-k Tabular Data Quality Insights
Manisha Padala, Lokesh Nagalapatti, Atharv Tyagi +2
We present an unsupervised method for aggregating anomalies in tabular datasets by identifying the top-k tabular data quality insights. Each insight consists of a set of anomalous…
PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect
Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh +1
Given a dataset of individuals each described by a covariate vector, a treatment, and an observed outcome on the treatment, the goal of the individual treatment effect (ITE) estima…