# Multicell experiments for marginal treatment effect estimation of digital ads

## TL;DR

This paper introduces a new experimental design and estimation method for digital advertising that accurately measures the impact of campaigns on consumer reach and spending, addressing limitations of existing approaches.

## Contribution

It proposes a novel multicell experimental design combined with modern estimation techniques to estimate marginal treatment effects in digital ads, especially under one-sided noncompliance.

## Key findings

- Demonstrates superior performance in simulations based on Facebook data.
- Enables decision-makers to optimize campaign reach and spending.
- Does not require additional budget or complex implementation.

## Abstract

Randomized experiments with treatment and control groups are an important tool to measure the impacts of interventions. However, in experimental settings with one-sided noncompliance extant empirical approaches may not produce the estimands a decision maker needs to solve the problem of interest. For example, these experimental designs are common in digital advertising settings but typical methods do not yield effects that inform the intensive margin: how many consumers should be reached or how much should be spent on a campaign. We propose a solution that combines a novel multicell experimental design with modern estimation techniques that enables decision makers to solve problems with an intensive margin. Our design is straightforward to implement and does not require additional budget. We illustrate our method through simulations calibrated using an advertising experiment at Facebook, demonstrating its superior performance in various scenarios and its advantage over direct optimization approaches.