OpportunityFinder: A Framework for Automated Causal Inference

TL;DR

OpportunityFinder is a user-friendly, code-less framework that automates causal inference studies on panel data, enabling non-experts to easily assess treatment impacts with minimal technical knowledge.

Contribution

It introduces a novel, easy-to-use framework that automates causal inference analysis, selecting appropriate algorithms dynamically for non-expert users using panel data.

Findings

Automates causal impact analysis with minimal user input

Supports multiple algorithms under a unified interface

Provides sensitivity and robustness analysis

Abstract

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.