Disruptive prediction with ordered treatment candidate bins
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Solution Overview
Problem
Conventional forecasting methods are inadequate for early decision-making in product or service lifecycles due to the need for sufficient data, and A/B testing can be impractical or unrepresentative, especially when dealing with disruptive treatments and non-representative user groups.
Innovation Solution
A machine learning model is trained using sequenced operations over ordered bins of treatment candidates, enabling nonlinear extrapolation to simulate missing control candidates and provide accurate predictions by propagating information from lower to higher propensity bins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional forecasting methods are used, then prediction accuracy improves with sufficient data, but early decisions cannot be made due to insufficient data availability
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical control candidate data before the disruptive treatment begins. This allows the model to learn patterns and relationships in advance, enabling accurate predictions even when treatment candidate data is scarce or unavailable during early stages.
Solution Approach 2:
The system creates synthetic control candidates by copying and adapting patterns from historical control data. The machine learning model generates predicted outcomes for treatment candidates by leveraging patterns learned from historical controls, effectively creating virtual control groups without requiring actual control candidates during the disruptive treatment.
2Measurement precision
If A/B testing is used to gather control data, then prediction accuracy improves, but user participation is reduced and sentiment is negatively impacted
Solution Approach 1:
The system extracts control candidate data from historical periods before the disruptive treatment began. By separating historical control data from current treatment candidates, the system can train models without prohibiting user participation in the actual product launch or treatment, thus avoiding negative sentiment while still obtaining sufficient control data for accurate predictions.
3Quantity of substance
If early adopters are used as control candidates, then data availability improves, but representativeness of the user base deteriorates
Solution Approach 1:
The system applies different quality standards to different data sources. Historical control candidates are used for training the machine learning model's structural understanding, while the model then adjusts predictions for treatment candidates based on their specific characteristics. This allows leveraging abundant historical data while accounting for differences in representativeness between early adopters and general user base.
Data Source
AI summary
Prediction of outcomes of disruptive treatments are enabled utilizing sequenced training of a machine learning model over ordered bins of treatment candidates. Treatment candidates may be assigned to candidate characterization bins with an ordering, and the model may be trained with a sequence of training steps corresponding to the ordering of the candidate characterization bins, in each training step the model having untreated candidate features from a corresponding bin and aggregate metrics from one or more previous steps as input. The predicted outcome for a selected bin may be generated with the trained model having treated candidate features and aggregate metrics from one or more previous steps as input. The predicted outcome may be a counterfactual prediction for a bin with insufficient control candidates, and may represent a nonlinear extrapolation from control data in prior bins in the bin ordering.