Long-Term Effect Estimation Using Instrumental Variable Regression
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Solution Overview
Problem
Estimating long-term causal effects is challenging due to the limitations of randomized controlled experiments, which are costly and have short durations, and observational data often includes unobserved confounding, leading to biased estimates.
Innovation Solution
A system and method that combines short-term experimental data from randomized controlled experiments with long-term observational data, using instrumental variable regression to estimate long-term effects while accounting for unobserved confounding, by computing a portion of the short-term outcome with no causal contribution from the treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If randomized controlled experiments are used to estimate long-term effects, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The patent segments the effect estimation process into two distinct phases: (1) a short-term randomized controlled experiment phase that captures treatment assignment and immediate outcomes, and (2) a long-term observational follow-up phase that tracks outcomes without active experiment control. This segmentation allows the beneficial randomized assignment to be captured in a short window while extending outcome observation indefinitely, resolving the contradiction between measurement precision and time loss.
Solution Approach 2:
The patent performs preliminary randomized treatment assignment in a short controlled experiment before the long-term outcome period begins. This preliminary action establishes causal identification through randomization, then allows observational data collection to proceed without the time-consuming overhead of maintaining experiment control throughout the entire long-term period, thereby reducing time loss while preserving measurement precision.
2Loss of time
If observational data is used to estimate long-term effects, then loss of time is reduced, but measurement precision deteriorates due to unobserved confounding
Solution Approach 1:
The patent uses the short-term experimental data as an intermediary to bridge the gap between observational data and causal inference. Specifically, it uses instrumental variable regression where the treatment assignment from the short-term experiment serves as an instrument that is correlated with the treatment in the long-term observational period but independent of unobserved confounders. This intermediary allows the patent to leverage efficient observational data collection while maintaining measurement precision through the causal identification provided by the experimental component.
3Measurement precision
If long-term randomized controlled experiments are conducted, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential causal identification component (randomized treatment assignment) from the full long-term experiment and separates it from the long-term data collection phase. By taking out only the critical randomized assignment step and performing it in a short initial period, the patent eliminates the need for complex ongoing experiment controlling operations while preserving the causal identification benefits, thereby reducing device complexity without sacrificing measurement precision.
Data Source
AI summary
Systems and methods for estimating a long-term effect in the presence of unobserved confounding are provided. For example, a long-term effect of a change in user interface of a media playback application may be estimated. An experimental dataset and an observational dataset may be compiled using data from a plurality of computing devices. The observational dataset may include unobserved confounding. Using the experimental dataset, a short-term effect may be determined. Using the short-term effect and samples from the observational dataset, an instrumental variable may be computed that may be used in instrumental variable regression to estimate the long-term effect.