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Found 7 papers, 0 papers with code
DRTCI: Learning Disentangled Representations for Temporal Causal Inference
Medical professionals evaluating alternative treatment plans for a patient often encounter time varying confounders, or covariates that affect both the future treatment assignment and the patient outcome.
CAMTA: Causal Attention Model for Multi-touch Attribution
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 ad campaigns via social networks, display ads, search etc.
Hi-CI: Deep Causal Inference in High Dimensions
The proposed architecture comprises of a decorrelation network and an outcome prediction network.
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks
Causal inference (CI) in observational studies has received a lot of attention in healthcare, education, ad attribution, policy evaluation, etc.
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population
Performing inference on data obtained through observational studies is becoming extremely relevant due to the widespread availability of data in fields such as healthcare, education, retail, etc.
Comparative Benchmarking of Causal Discovery Techniques
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) allowing both cycles and latent variables, along with experimental results comparing them from three perspectives: (a) structural accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual inference.
Deep Convolutional Neural Networks for Pairwise Causality
Discovering causal models from observational and interventional data is an important first step preceding what-if analysis or counterfactual reasoning.
