Currently, azcausal provides two well-known and widely used causal inference methods: Difference-in-Difference (DID) and Synthetic Difference-in-Difference (SDID ...
BNN, BLR Johansson, Fredrik, Uri Shalit, and David Sontag. "Learning representations for counterfactual inference." 33rd International Conference on Machine Learning ...
Abstract: Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not ...
Abstract: Modern cyber-physical systems would often fall victim to unanticipated anomalies. Humans are still required in many operations to troubleshoot and respond to such anomalies, such those in ...
Scott Brinker’s Martech for 2026 report offers a lucid map of the terrain GTM teams must now navigate: a marketplace no longer defined by sequential buyer journeys, increasingly shaped by agentic AI, ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback