Gil Tabak
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Many digital advertisers continue to rely on attribution models to estimate the effectiveness of their marketing spend, allocate budget, and guide bidding decisions for real time auctions. The work described in this paper builds on previous efforts to better understand the capabilities and limitations of attribution models using simulated path data with experiment-based ground truth. While previous efforts were based on a generic specification of user path characteristics (e.g., ad channels considered, observed events included, and the transition rates between observed events), here we generalize the process to include a pre-analysis optimization step that matches the characteristics of the simulated path data with a set of reference path data from a particular advertiser. An attribution model analysis conducted with path-matched data is more relevant and applicable to an advertiser than generic path data. We demonstrate this path-fitting process using data from Booking.com. The simulated matched paths are used to demonstrate a few key capabilities and limitations for several position-based attribution models.
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Profiling cellular phenotypes from microscopic imaging can provide meaningful biological information resulting from various factors affecting the cells. One motivating application is drug development: morphological cell features can be captured from images, from which similarities between different drug compounds applied at different doses can be quantified. The general approach is to find a function mapping the images to an embedding space of manageable dimensionality whose geometry captures relevant features of the input images. An important known issue for such methods is separating relevant biological signal from nuisance variation. For example, the embedding vectors tend to be more correlated for cells that were cultured and imaged during the same week than for those from different weeks, despite having identical drug compounds applied in both cases. In this case, the particular batch in which a set of experiments were conducted constitutes the domain of the data; an ideal set of image embeddings should contain only the relevant biological information (e.g., drug effects). We develop a general framework for adjusting the image embeddings in order to “forget” domain-specific information while preserving relevant biological information. To achieve this, we minimize a loss function based on distances between marginal distributions (such as the Wasserstein distance) of embeddings across domains for each replicated treatment. For the dataset we present results with, the only replicated treatment happens to be the negative control treatment, for which we do not expect any treatment-induced cell morphology changes. We find that for our transformed embeddings (i) the underlying geometric structure is not only preserved but the embeddings also carry improved biological signal; and (ii) less domain-specific information is present.
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