Online Causal Inference for Advertising in Real-Time Bidding Auctions

TL;DR

This paper introduces a novel causal inference method for online advertising in real-time bidding auctions, using an adapted Thompson sampling algorithm to accurately estimate advertising effects while minimizing experimental costs.

Contribution

It presents a new approach leveraging auction structure to identify advertising effects through optimal bids, with an efficient algorithm and theoretical regret bounds.

Findings

Outperforms existing methods in estimating advertising effects.

Successfully recovers optimal bids in RTB auctions.

Demonstrates order-optimal regret bounds for the proposed algorithm.

Abstract

Real-time bidding (RTB) systems, which utilize auctions to allocate user impressions to competing advertisers, continue to enjoy success in digital advertising. Assessing the effectiveness of such advertising remains a challenge in research and practice. This paper proposes a new approach to perform causal inference on advertising bought through such mechanisms. Leveraging the economic structure of first- and second-price auctions, we first show that the effects of advertising are identified by the optimal bids. Hence, since these optimal bids are the only objects that need to be recovered, we introduce an adapted Thompson sampling (TS) algorithm to solve a multi-armed bandit problem that succeeds in recovering such bids and, consequently, the effects of advertising while minimizing the costs of experimentation. We derive a regret bound for our algorithm which is order optimal and use data from RTB auctions to show that it outperforms commonly used methods that estimate the effects of advertising.