Apmplitude, Inc.

Scott Kramer

Muhammad Bilal Mahmood

Eric Pollmann

Cynthia Rogers

Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user's performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output. A treatment effect is then computed for a treatment action by generating a simulated treatment matrix where all values for the treatment action are set to true, computing an average of outputs from the machine learning system using the simulated treatment matrix, generating a simulated control matrix where all values for the treatment action are set to false, computing an average of outputs from the machine learning system using the simulated control matrix, and computing a difference between the two average outputs.

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New York, NY, United States of America

Scott Kramer