1 citations · 1 across the 6 of their papers we have counts for
6 papers
Identifying Structural Biases from Causal Mechanism Shifts
Praharsh Nanavati, Jilles Vreeken, David Kaltenpoth
Causal discovery methods commonly assume that all data is independently and identically distributed (i.i.d.) and that there are no unmeasured variables affecting the system. In pra…
Cost Efficient Fairness Audit Under Partial Feedback
Nirjhar Das, Mohit Sharma, Praharsh Nanavati +2
We study the problem of auditing the fairness of a given classifier under partial feedback, where true labels are available only for positively classified individuals, (e.g., loan…
Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
Praharsh Nanavati, Ranjitha Prasad, Karthikeyan Shanmugam
Estimating treatment effects from observational data is challenging due to two main reasons: (a) hidden confounding, and (b) covariate mismatch (control and treatment groups not ha…
CLIMAX: An exploration of Classifier-Based Contrastive Explanations
Praharsh Nanavati, Ranjitha Prasad
Explainable AI is an evolving area that deals with understanding the decision making of machine learning models so that these models are more transparent, accountable, and understa…
Open problems in causal structure learning: A case study of COVID-19 in the UK
Anthony Constantinou, Neville K. Kitson, Yang Liu +5
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algor…
Bowlership: Examining the Existence of Bowler Synergies in Cricket
Praharsh Nanavati, Amit Anil Nanavati
Player synergies are a salient feature of team sports. In the team game of cricket, player synergies may be reflected in batting partnerships. Batting partnerships have been analys…