Estimating Ad Impact on Clicker Conversions for Causal Attribution: A Potential Outcomes Approach

@inproceedings{Barajas2015EstimatingAI,
  title={Estimating Ad Impact on Clicker Conversions for Causal Attribution: A Potential Outcomes Approach},
  author={Joel Barajas and Ram Akella and Aaron Flores and Marius Holtan},
  booktitle={SDM},
  year={2015}
}
We analyze the causal effect of online ads on the conversion probability of the users who click on the ad (clickers). We show that designing a randomized experiment to find this effect is infeasible, and propose a method to find the local effect on the clicker conversions. This method is developed in the Potential Outcomes causal model, via Principal Stratification to model non-ignorable post-treatment (or endogenous) variables such as user clicks, and is validated with simulated data. Based on… 

Figures and Tables from this paper

Matching via Dimensionality Reduction for Estimation of Treatment Effects in Digital Marketing Campaigns

TLDR
A novel estimator is proposed that first projects the data to a number of random linear subspaces, and it then estimates the median treatment effect by nearest-neighbor matching in each subspace.

Incrementality Testing in Programmatic Advertising: Enhanced Precision with Double-Blind Designs

TLDR
This work presents a novel randomized design solution for incrementality testing based on ghost bidding with improved measurement precision that provides faster and cheaper results including double-blind, to the users and to the serving engine, post-auction experiment execution without ad targeting bias.

Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy

This paper presents a viewpoint on an emerging dichotomy in data science: applications in which predictions of datadriven algorithms are used to support people in making consequential decisions that

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

TLDR
Topics covered include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimization, statistical arbitrage, dynamic pricing, and ad fraud detection are an invaluable text for researchers and practitioners alike.

Knowledge Extraction and Retrieval for Domain-Specific Documents

TLDR
This study proposes a synergistic approach to extracting disorder concepts and variants in the healthcare domain, and observes significant improvements of service time responsiveness during knowledge extraction and retrieval in the networking service center context at Cisco.

References

SHOWING 1-10 OF 31 REFERENCES

Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces

TLDR
Two novel randomized designs are proposed to estimate the overall campaign attribution without placebo ads, and disaggregate the campaign presence and ad effects, and show the ex-ante value of continuing evaluation to enhance the user selection for ad exposure mid-flight.

Dynamic effects of ad impressions on commercial actions in display advertising

TLDR
A time series approach, based on Dynamic Linear Models (DLM), to estimate the impact of ad impressions on the daily number of commercial actions when no user tracking is possible, and finds that the output of the proposed method is consistent with the results of A/B testing with similar confidence intervals.

Here, there, and everywhere: correlated online behaviors can lead to overestimates of the effects of advertising

TLDR
Using three controlled experiments, it is shown that observational data frequently lead to incorrect estimates of adFX, and how and why observational methods lead to a massive overestimate of adfx in such circumstances.

Marketing campaign evaluation in targeted display advertising

TLDR
An experimental analysis to estimate the causal effect of online marketing campaigns as a whole, and not just the media ad design, and the causal effects on user conversion probability is developed.

Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with “Censoring” Due to Death

Causal inference is best understood using potential out- comes. This use is particularly important in more complex settings, that is, observational studies or randomized experiments with compli-

Evaluating online ad campaigns in a pipeline: causal models at scale

TLDR
Simulations based on realistic scenarios show that the resulting estimates are more robust to selection bias than traditional alternatives, such as regression modeling or propensity scoring.

Causal Inference Using Potential Outcomes

Causal effects are defined as comparisons of potential outcomes under different treatments on a common set of units. Observed values of the potential outcomes are revealed by the assignment

An efficient framework for online advertising effectiveness measurement and comparison

TLDR
This framework is applied to an online campaign involving millions of unique users, which shows substantially better model fitting and efficiency, and can be further generalized to comparison of multiple treatments and more general treatment regimes.

The Effect of Banner Advertising on Internet Purchasing

This article focuses on whether banner advertising affects purchasing patterns on the Internet. Using a behavioral database that consists of customer purchases at a Web site along with individual

Bayesian inference for causal effects in randomized experiments with noncompliance

For most of this century, randomization has been a cornerstone of scientific experimentation, especially when dealing with humans as experimental units. In practice, however, noncompliance is