A multi-cell experimental design to recover policy relevant treatment effects, with an application to online advertising

Caio Waisman; Brett R. Gordon

AI summary · arXiv: 2302.13857v1 · econ.EM

A multi-cell experimental design to recover policy relevant treatment effects, with an application to online advertising

Caio Waisman, Brett R. Gordon

Original paper Submitted to arXiv on 27 Feb. 2023 License: CC BY 4.0

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The essentials

The paper proposes an experimental design that leverages modern estimation techniques when only eligibility to receive treatment can be randomized.
This design is tailored to exploit these methods and recover enough information to solve problems with an intensive margin.
The paper fills the gap in the literature by embedding a causal inference setup in an advertiser's optimization problem, enabling them to choose the intensive margin of how many consumers to reach with advertising.
It connects causal inference with advertising decisions and provides a framework for recovering treatment effect parameters that account for parallel experimentation by competitors.
The paper introduces a specific decision problem faced by firms - choosing the fraction of consumers from a target segment to reach with advertising to maximize expected profit.
The proposed approach outperforms standard techniques in recovering treatment effect parameters and generates better decisions relative to standard techniques through a simple advertising reach decision problem.
The paper discusses practical issues in implementing this design and highlights its limitations in accounting for other experimentation costs relevant in real-time bidding settings.

Key points written by AI: they may contain errors, check them against the paper.

AI-generated keywords: Causal Inference Advertising Decisions Experimental Design Marginal Treatment Effects Real-time Bidding

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Comprehensive Summary
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Original abstract

The paper makes three contributions to the literature. Firstly, it proposes an experimental design that leverages modern estimation techniques when only eligibility to receive treatment can be randomized. This design is tailored to exploit these methods and recover enough information to solve problems with an intensive margin. Secondly, the paper fills the gap in the literature by embedding a causal inference setup in an advertiser's optimization problem, enabling them to choose the intensive margin of how many consumers to reach with advertising. Thirdly, it connects causal inference with advertising decisions and provides a framework for recovering treatment effect parameters that account for parallel experimentation by competitors. The paper introduces a specific decision problem faced by firms - choosing the fraction of consumers from a target segment to reach with advertising to maximize expected profit. The typical experimental design with one-sided noncompliance does not provide enough information needed to solve this problem as it only produces estimates of intent-to-treat or average treatment effects. To address this issue, the paper proposes a novel multi-cell experimental design combined with modern estimation techniques that enable decision-makers to recover an approximation of marginal treatment effects (MTE) function. Using data from Facebook advertising experiments, the proposed approach outperforms standard techniques in recovering treatment effect parameters and generates better decisions relative to standard techniques through a simple advertising reach decision problem. The paper also discusses practical issues in implementing this design and highlights its limitations in accounting for other experimentation costs relevant in real-time bidding settings. Overall, this paper contributes significantly to estimating treatment effects in experiments with one-sided noncompliance and provides insights into solving complex advertising decision problems using causal inference frameworks.

Exploring the Benefits of Modern Estimation Techniques for Advertising Reach Decisions

Advertising is a key component of any successful business strategy, and firms need to make decisions about how many consumers from their target segment to reach with advertising in order to maximize expected profit. However, traditional experimental designs with one-sided noncompliance do not provide enough information needed to solve this problem as they only produce estimates of intent-to-treat or average treatment effects. To address this issue, a recent research paper proposes a novel multi-cell experimental design combined with modern estimation techniques that enable decision-makers to recover an approximation of marginal treatment effects (MTE) function. This article will explore the benefits of using modern estimation techniques when only eligibility to receive treatment can be randomized and discuss practical issues in implementing this design as well as its limitations in accounting for other experimentation costs relevant in real-time bidding settings.

Proposed Experimental Design

The proposed experimental design leverages modern estimation techniques when only eligibility to receive treatment can be randomized. This design is tailored to exploit these methods and recover enough information to solve problems with an intensive margin - i.e., choosing the fraction of consumers from a target segment to reach with advertising - by embedding a causal inference setup into an advertiser's optimization problem. The paper introduces two main components: 1) A multi-cell experiment that consists of four cells representing different levels of ad exposure; 2) An estimation technique based on inverse propensity score weighting (IPW).

The multi-cell experiment provides more granular data than traditional experiments by allowing for multiple levels of exposure instead of just two (treatment vs control). For example, if we are interested in determining how much additional revenue is generated by increasing ad exposure from 10% up to 30%, then we would create four cells: 0%-10%, 10%-20%, 20%-30%, and 30%-40%. By comparing outcomes across these cells, it becomes possible to estimate MTEs at each level which can then be used for optimization purposes.

Inverse Propensity Score Weighting (IPW), on the other hand, allows us to adjust for potential selection bias caused by differences between groups receiving different treatments due factors such as demographics or past behavior patterns. IPW works by assigning higher weights (or importance) on observations where individuals were less likely than average people within their group/segment/etc., so that those observations have more influence on our results compared with those who were more likely than average people within their group/segment/etc..

Connecting Causal Inference With Advertising Decisions

This paper connects causal inference with advertising decisions and provides a framework for recovering treatment effect parameters that account for parallel experimentation by competitors. Using data from Facebook advertising experiments, the proposed approach outperforms standard techniques in recovering treatment effect parameters and generates better decisions relative to standard techniques through a simple advertising reach decision problem.

Practical Issues & Limitations

While this approach has been shown effective at solving complex decision problems related to ad targeting strategies, there are still some practical issues associated with its implementation such as setting up the appropriate infrastructure or dealing with potential privacy concerns when collecting user data from online platforms like Facebook or Google Ads. Additionally, it should also be noted that while this approach does provide insights into estimating treatment effects in experiments with one sided noncompliance it does not account for all experimentation costs relevant in real time bidding settings such as bid adjustments or budget constraints which may limit its effectiveness when applied outside controlled lab environments .

Conclusion

Overall, this paper contributes significantly towards understanding how firms can leverage modern estimation techniques when making decisions about reaching certain segments through targeted ads campaigns while taking into consideration both practical considerations and competitive pressures faced during real time bidding scenarios . It fills an important gap in literature connecting causal inference frameworks directly into advertiser’s optimization problems enabling them choose optimal intensive margins while maximizing expected profits .

Created on 10 Apr. 2023

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A multi-cell experimental design to recover policy relevant treatment effects, with an application to online advertising

The essentials

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Definitions

Exploring the Benefits of Modern Estimation Techniques for Advertising Reach Decisions

Proposed Experimental Design

Connecting Causal Inference With Advertising Decisions

Practical Issues & Limitations

Conclusion

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