Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement
References
- (1996) Identification and estimation of causal effects using instrumental variables. J. Amer. Statist. Assoc. 91(434):444–455.Crossref, Google Scholar
- (2019) Machine learning methods that economists should know about. Annual Rev. Econom. 11:685–725.Crossref, Google Scholar
- (2019) Generalized random forests. Ann. Statist. 47(2):1148–1178.Crossref, Google Scholar
- (2015) Consumer heterogeneity and paid search effectiveness: A large-scale field experiment. Econometrica 83(1):155–174.Crossref, Google Scholar
- (2018) Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–C68.Crossref, Google Scholar
- (2016) People and cookies: Imperfect treatment assignment in online experiments. WWW ’16: Proceedings of the 25th International Conference on World Wide Web, 1103–1111.Google Scholar
- (1977) Statistical Power Analysis for the Behavioral Sciences (Lawrence Erlbaum Associates, Inc., Mahwah, NJ).Google Scholar
- (2002) Propensity score matching methods for non-experimental causal studies. Rev. Econom. Stat. 84(1):151–161.Crossref, Google Scholar
- (2019) Causally driven incremental multi touch attribution using a recurrent neural network. Preprint, submitted February 1, https://arxiv.org/abs/1902.00215.Google Scholar
- (2022) Causal decision making and causal effect estimation are not the same…and why it matters. INFORMS J. Data Sci. 1(1):4–16.Link, Google Scholar
- (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models, 1st ed. (Cambridge University Press, Cambridge, United Kingdom).Crossref, Google Scholar
- (2021) Inefficiencies in digital advertising markets. J. Marketing 85(1):7–25.Crossref, Google Scholar
- (2022) Predicting incrementality by experimentation for ad measurement. Working paper, Kellogg School of Management, Evanston, IL.Google Scholar
- (2019) A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Sci. 38(2):193–225.Link, Google Scholar
- (2017) pdp: An R package for constructing partial dependence plots. R J. 9(1):421–436.Crossref, Google Scholar
- (2021) Auction throttling and causal inference of online advertising effects. Preprint, submitted December 30, https://arxiv.org/abs/2112.15155.Google Scholar
- (2017) Deep IV: A flexible approach for counterfactual prediction. Precup D, Teh YW, eds. Proc. 34th Internat. Conf. Machine Learning, vol. 70 of Proc. Machine Learning Res., 1414–1423.Google Scholar
- (1997) Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Rev. Econom. Stud. 64(4):605–654.Crossref, Google Scholar
- (2018) Measuring display advertising response using observational data: The impact of selection biases. Preprint, submitted October 1, http://dx.doi.org/10.2139/ssrn.3264871.Google Scholar
- (2015) Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. 1st ed. (Cambridge University Press, Cambridge, United Kingdom).Crossref, Google Scholar
- (2004) Nonparametric estimation of average treatment effects under exogeneity: A review. Rev. Econom. Stat. 86(1):4–29.Crossref, Google Scholar
- (1994) Identification and estimation of local average treatment effects. Econometrica 62(2):467–475.Crossref, Google Scholar
- (2009) Recent developments in the econometrics of program evaluation. J. Econom. Lit. 47(1):5–86.Crossref, Google Scholar
- (2022) Inferno: A guide to field experiments in online display advertising. Preprint, submitted July 18, http://dx.doi.org/10.2139/ssrn.3581396.Google Scholar
- (2017a) Ghost ads: Improving the economics of measuring ad effectiveness. J. Marketing Res. 54(6):867–884.Crossref, Google Scholar
- (2017b) The online display ad effectiveness funnel & carryover: Lessons from 432 field experiments. Preprint, submitted October 1, http://dx.doi.org/10.2139/ssrn.2701578.Google Scholar
- (2020) Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing, 1st ed. (Cambridge University Press, Cambridge, United Kingdom).Crossref, Google Scholar
- (1986) Evaluating the econometric evaluations of training programs with experimental data. Amer. Econom. Rev. 76(4):604–620.Google Scholar
- (2011) Here, there, and everywhere: Correlated online behaviors can lead to overestimates of the effects of advertising. Proc. 20th Internat. Conf. World Wide Web (Association for Computing Machines), 157–166.Google Scholar
- (2015) Measuring the effects of advertising: The digital frontier. Goldfarb A, Greenstein S, Tucker C, eds. Economic Analysis of the Digital Economy, Chapter 7 (University of Chicago Press, Chicago), 191–218.Crossref, Google Scholar
- (2018) Incrementality bidding and attribution. Preprint, submitted February 27, https://ssrn.com/abstract=3129350.Google Scholar
- (2022) Frontiers: The identity fragmentation bias. Marketing Sci. 41(3):433–440.Link, Google Scholar
- . (2019) Deep learning recommendation model for personalization and recommendation systems. Preprint, submitted May 31, https://arxiv.org/abs/1906.00091.Google Scholar
- (1983) The central role of the propensity score in observational studies for causal effects. Biometrica 70:41–55.Crossref, Google Scholar
- (1978) Bayesian inference for causal effects: The role of randomization. Ann. Statist. 6:34–58.Crossref, Google Scholar
- (2020) Experimentation and performance in advertising: An observational survey of firm practices on Facebook. Expert Syst. Appl. 158:113554.Crossref, Google Scholar
- (2021) The role of randomized control trials in online demand generation: Exploratory evidence from Facebook. Proceedings of the International Conference on Information Systems (ICIS). Preprint, submitted September 27, http://dx.doi.org/10.2139/ssrn.3794028.Google Scholar
- (2020) Facebook conversion lift measurement issue goes undetected for 12 months. https://www.adexchanger.com/platforms/facebook-conversion-lift-measurement-issue-goes-undetected-for-12-months/Google Scholar
- (2021) TV advertising effectiveness and profitability: Generalizable results from 288 brands. Econometrica 89(4):1855–1879.Crossref, Google Scholar
- (2010) Matching methods for causal inference: A review and a look forward. Statist. Sci. 25(1):1–21.Crossref, Google Scholar
- . (2021) Causal inference and machine learning in practice with econml and causalml: Industrial use cases at Microsoft, TripAdvisor, Uber. KDD ’21: Proc. 27th ACM SIGKDD Conf. Knowledge Discovery Data Mining, 4072–4073.Google Scholar
- (2022) Display ad measurement using observational data: A reinforcement learning approach. Working paper, Duke University, Durham, NC.Google Scholar
- (2022) Online causal inference for advertising in real-time bidding auctions. Preprint, submitted August 22, https://arxiv.org/abs/1908.08600.Google Scholar

