  Traditionally, data scientists use exploratory data analysis techniques such
as correlation analysis, summary statistics, and regression analysis for
identifying the most product enhancements and roadmap planning. However, these
conventional approaches often yield biased conclusions and suboptimal
solutions, leading to a waste of valuable time and missed opportunities for
higher-value outcomes. In contrast, there are alternative techniques that
involve the use of causal inference methods. However, these methods suffer from
issues of limited accessibility, as they are not easily understandable or
effectively utilized by inexperienced practitioners. Additionally, their
implementation necessitates a substantial investment of time and effort. To
this end, this paper tackles these challenges by democratizing one of the
causal inference methods called Propensity Score Matching (PSM) and enhancing
its accessibility for less technically inclined users through the automation of
the entire workflow using a web application. Our approach not only fills this
accessibility gap but also contributes to the existing literature by
introducing a more rigorous model selection process and an enhanced sensitivity
analysis. By overcoming the limitations of traditional exploratory data
analysis methods, our web application has empowered data scientists at
Booking.com to make better use of PSM, thereby improving the overall efficacy
of their analyses.
