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.

翻译：我们提出OpportunityFinder，一个面向非专业用户、基于面板数据执行多种因果推断研究的无代码框架。当前版本仅要求用户提供原始观测数据及配置文件，系统将自动触发数据检查/处理流程，选择适宜的算法执行因果分析，并返回处理对设定结果变量的因果效应及敏感性/稳健性检验结果。因果推断被广泛用于量化用户与产品功能交互的后续影响，此类研究通常由科学家或经济学家定期执行。业务利益相关者的工作往往受限于科学家或经济学家的研究产能。我们提出的OpportunityFinder针对常见因果研究场景提供四项核心特性：（1）业务分析师与科学家均能轻松使用，（2）通过统一输入/输出接口抽象多种算法，（3）支持面板数据下二元处理变量的因果效应分析，（4）根据数据规模动态选择算法。