Deep Generative Model Task Embedding for Treatment Effect Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional digital content distribution systems face inaccuracies, inefficiencies, and inflexibility in estimating causal effects of digital content across multiple channels due to incomplete datasets, selection biases, and exponential computational requirements, failing to account for interdependence between treatments and latent confounders.
Innovation Solution
A deep generative model with a task embedding layer is employed to estimate causal effects of digital content subsets on client devices, using a variational autoencoder to model interdependence and scale efficiently across multiple treatments, accounting for latent confounders and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems treat each combination of distribution channels as separate treatments, then they can estimate effects of individual treatments, but they fail to accurately capture interdependence between individual treatments and lead to inaccurate results
Solution Approach 1:
The patent merges the modeling of interdependence between distribution channels by using a joint probability distribution that simultaneously models multiple treatment effects and their interactions. This combines separate treatment models into a unified framework that captures interdependencies without requiring separate models for each combination.
Solution Approach 2:
The patent creates a composite modeling approach by integrating multiple probability distributions (treatment effects, interdependence structures, and outcome models) into a unified joint distribution framework. This composite model synthesizes multiple components to achieve accurate treatment effect estimation while capturing interdependencies.
2Productivity
If conventional systems utilize observational data to estimate effects of applied actions, then they can distribute content without randomization, but they are subject to selection biases and overestimate effects of digital content distribution
Solution Approach 1:
The patent transforms observational data into a framework that accounts for selection biases by changing the parameters of the probability distribution to include latent confounders. This allows the model to adjust for non-random selection effects while maintaining the efficiency of using observational data rather than requiring randomized controlled trials.
3Adaptability or versatility
If conventional systems use separate private neural network channels for each action, then they can model individual treatments, but extending to multiple actions explodes the network size and the number of parameters to learn
Solution Approach 1:
The patent creates a universal joint probability distribution framework that can handle any number of treatments and their combinations through a single unified model. This multi-functional approach allows the same model structure to estimate effects of individual treatments, combinations of treatments, and their interdependencies without requiring separate neural network channels for each action.
4Measurement precision
If conventional systems require significant time and computing resources to analyze effects of combination of actions, then they can estimate treatment effects, but they are inefficient and cannot scale to large numbers of potential actions
Solution Approach 1:
The patent segments the complex problem of estimating treatment effects into manageable probability distribution components (treatment effects, interdependence structures, and outcome models). This segmentation allows efficient computation by breaking down the exponential complexity into structured probabilistic relationships that can be computed more efficiently using Bayesian inference techniques.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for training and utilizing a generative machine learning model to select one or more treatments for a client device from a set of treatments based on digital characteristics corresponding to the client device. In particular, the disclosed systems can train and apply a variational autoencoder with a task embedding layer that generates estimated effects for treatment combinations. For example, the disclosed systems receive, as input, digital characteristics corresponding to the client device and various treatment combinations. The disclosed systems apply the trained generative machine learning model with the task embedding layer to the digital characteristics to generate effect estimations for the various treatment combinations. Based on the effect estimations for the treatment combinations, the disclosed systems select one or more treatments to provide to the client device.