12 citations · 30 across the 7 of their papers we have counts for
8 papers
DRTCI: Learning Disentangled Representations for Temporal Causal Inference
Garima Gupta, Lovekesh Vig, Gautam Shroff
Medical professionals evaluating alternative treatment plans for a patient often encounter time varying confounders, or covariates that affect both the future treatment assignment…
CAMTA: Causal Attention Model for Multi-touch Attribution
Sachin Kumar, Garima Gupta, Ranjitha Prasad +3
Advertising channels have evolved from conventional print media, billboards and radio advertising to online digital advertising (ad), where the users are exposed to a sequence of a…
Hi-CI: Deep Causal Inference in High Dimensions
Ankit Sharma, Garima Gupta, Ranjitha Prasad +3
We address the problem of counterfactual regression using causal inference (CI) in observational studies consisting of high dimensional covariates and high cardinality treatments.…
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks
Ankit Sharma, Garima Gupta, Ranjitha Prasad +3
Causal inference (CI) in observational studies has received a lot of attention in healthcare, education, ad attribution, policy evaluation, etc. Confounding is a typical hazard, wh…
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population
Ankit Sharma, Garima Gupta, Ranjitha Prasad +3
Performing inference on data obtained through observational studies is becoming extremely relevant due to the widespread availability of data in fields such as healthcare, educatio…
Comparative Benchmarking of Causal Discovery Techniques
Karamjit Singh, Garima Gupta, Vartika Tewari +1
In this paper we present a comprehensive view of prominent causal discovery algorithms, categorized into two main categories (1) assuming acyclic and no latent variables, and (2) a…