OpportunityFinder: A Framework for Automated Causal Inference
Huy Nguyen Prince Grover Devashish Khatwani
We introduce OpportunityFinder, a code-less framework for performing a
variety of causal inference studies with panel data for non-expert users. In
its current state, OpportunityFinder only requires users to provide raw
observational data and a configuration file. A pipeline is then triggered that
inspects/processes data, chooses the suitable algorithm(s) to execute the
causal study. It returns the causal impact of the treatment on the configured
outcome, together with sensitivity and robustness results. Causal inference is
widely studied and used to estimate the downstream impact of individual's
interactions with products and features. It is common that these causal studies
are performed by scientists and/or economists periodically. Business
stakeholders are often bottle-necked on scientist or economist bandwidth to
conduct causal studies. We offer OpportunityFinder as a solution for commonly
performed causal studies with four key features: (1) easy to use for both
Business Analysts and Scientists, (2) abstraction of multiple algorithms under
a single I/O interface, (3) support for causal impact analysis under binary
treatment with panel data and (4) dynamic selection of algorithm based on scale
of data.