Abstract illustration of a gavel striking a cityscape with glowing ad displays, representing online advertising auctions.

Bidding Smarter: How Online Causal Inference is Revolutionizing Ad Auctions

Nico Varela in Tech & Innovation June 2025 • 4 min read.

"Unlock the secrets of real-time bidding with causal inference and multi-armed bandit algorithms for optimal advertising effectiveness."

In today's digital landscape, real-time bidding (RTB) systems are the backbone of digital advertising, utilizing auctions to allocate user impressions to competing advertisers. These systems have revolutionized how ads are bought and sold, yet assessing the true effectiveness of this advertising remains a significant challenge.

The complexity of RTB auctions, characterized by vast inventories, rapid transactions, and intricate competitive dynamics, introduces substantial uncertainty for advertisers. To navigate this environment successfully, advertisers need accurate and reliable methods to measure the value of their ad campaigns, optimize their bidding strategies, and ensure a strong return on investment.

Recent research introduces a novel approach to causal inference in RTB advertising, leveraging the economic structures of first- and second-price auctions to identify the effects of advertising. This method uses an adapted Thompson Sampling algorithm to solve a multi-armed bandit problem, recovering optimal bids while minimizing the costs of experimentation. This article explores the potential of this approach, offering insights for marketers and advertisers looking to enhance their online advertising strategies.

Understanding Online Causal Inference in RTB Auctions

Abstract illustration of a gavel striking a cityscape with glowing ad displays, representing online advertising auctions.

The core challenge in RTB advertising is determining whether ad exposure truly influences user behavior, and to what extent. Simply comparing outcomes between users who see ads and those who don't is problematic because ad exposure isn't random; it's determined by an auction. This means advertisers can't easily implement standard experimental designs where ad exposure is directly controlled.

To tackle this, a new approach focuses on the advertiser's bid as the control variable. Instead of directly manipulating ad exposure, the experimenter controls only the bid price, using it to measure the resulting stochastic outcome of ad exposure. This shifts the focus from directly randomizing ad exposure to understanding the effect of the bid on ad exposure and, ultimately, on user behavior.

First-Price Auctions (FPAs): Here, the highest bidder pays their bid price.
Second-Price Auctions (SPAs): The highest bidder wins but pays the price of the second-highest bid.
The Economic Challenge: Accurately assessing the value of each impression given the uncertainty about user value and competitive landscape.
The Experimental Challenge: Managing the cost of experimentation, considering both overbidding (wasting resources) and underbidding (missing valuable opportunities).

Online methods, which introduce randomization to explore advertising value while exploiting learned information to optimize bids, are particularly attractive. These methods balance the need to learn the average treatment effect (ATE) of ad exposure with the goal of optimizing the bidding policy—a dual challenge that requires careful consideration.

The Future of Ad Auctions: Smarter, More Effective Strategies

The research presented here offers a promising pathway toward more effective and efficient online advertising. By combining causal inference techniques with adaptive algorithms, advertisers can gain a deeper understanding of their campaigns' true impact while optimizing their bidding strategies to maximize ROI. As the digital advertising landscape continues to evolve, these innovative approaches will be essential for staying ahead of the curve and achieving sustainable success.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.1908.086,

Title: Online Causal Inference For Advertising In Real-Time Bidding Auctions

Subject: cs.lg cs.gt econ.em stat.ml

Authors: Caio Waisman, Harikesh S. Nair, Carlos Carrion

Published: 22-08-2019

Everything You Need To Know

What is the role of real-time bidding (RTB) in digital advertising?

Real-time bidding (RTB) is a core component of digital advertising, acting as the mechanism through which ad impressions are allocated to advertisers. In an RTB system, auctions occur to determine which advertisers' ads are displayed to users. The process is characterized by rapid transactions and complex competition. This system facilitates the buying and selling of ads in real-time, but assessing the effectiveness of these ads remains a challenge due to the inherent complexities of the auction environment.

How does causal inference help improve advertising strategies in RTB auctions?

Causal inference provides a method to assess the actual influence of ad exposure on user behavior within the complex environment of RTB auctions. By focusing on the advertiser's bid as a control variable, the method allows to measure the stochastic outcome of ad exposure. This means that instead of directly controlling ad exposure, the experimenter manipulates the bid price to understand its impact on ad exposure and, subsequently, on user behavior. This approach allows for more accurate measurement of ad campaign value and the optimization of bidding strategies, aiming for a higher return on investment.

What are the key differences between First-Price Auctions (FPAs) and Second-Price Auctions (SPAs), and how do they affect bidding strategies?

In First-Price Auctions (FPAs), the highest bidder wins and pays the price they bid. This structure encourages advertisers to bid conservatively, as overbidding directly translates to higher costs. Conversely, in Second-Price Auctions (SPAs), the highest bidder still wins, but pays the price of the second-highest bid. This mechanism can encourage more aggressive bidding, as advertisers are less likely to overpay relative to their perceived value of the impression. The choice between these auction types significantly influences how advertisers formulate their bids, impacting both their costs and the likelihood of winning ad impressions.

How do multi-armed bandit algorithms and Thompson Sampling contribute to optimal bidding in RTB advertising?

Multi-armed bandit algorithms and Thompson Sampling help advertisers solve the exploration-exploitation dilemma inherent in online advertising. These algorithms introduce randomization to explore the value of advertising while using learned information to optimize bids. Thompson Sampling, in particular, is an adaptive algorithm that minimizes experimentation costs by balancing the need to learn the average treatment effect (ATE) of ad exposure with the goal of optimizing the bidding policy. The goal is to find the optimal bids while minimizing the costs of experimentation, thus improving the return on investment.

What are the main challenges advertisers face in RTB auctions, and how can online causal inference address them?

Advertisers in RTB auctions encounter several significant challenges, including the uncertainty in determining whether ad exposure truly influences user behavior, and in what extent. They also face the economic and experimental challenges of accurately assessing the value of each impression, given the competitive landscape, while managing the costs of experimentation to avoid overbidding and underbidding. Online causal inference offers a novel approach by using the advertiser's bid as the control variable and adapting algorithms to optimize bids. This approach allows advertisers to get a deeper understanding of their campaigns' impact while optimizing their bidding strategies to maximize ROI.