Read it for
- Causal inference
- Incrementality
- Experiments
What this book covers
Zezhen (Dawn) He and Vithala R. Rao survey methods for answering causal questions in marketing. The material is relevant when a client asks what advertising changed, rather than what an attribution report assigned to it. Our source review covered the public sample, so use the listing to assess fit before committing to the full technical work.
Bring it to your work
Write the treatment, outcome, confounders and identifying assumption for one client measurement problem. This is a PMC editorial prompt.
Reading context
Public 52-page sample inspected, not the complete monograph. Statistical prerequisites apply; binding format remains unverified.
About this reading guide
This guide draws on the source description, contents or sample identified in the edition notes. No external review is cited for this listing.
Prepared with AI assistance from the sources identified on this page. The applications and comparisons are Paid Media Collective suggestions, not claims of full-book reading. An external review does not verify every claim in a book.
Another book to consider
Causal Inference: The Mixtape →
Compare this for worked causal-inference examples in a broader social-science setting.
Edition & source notes
The reference below identifies the material checked for this listing. It may differ from the format offered by the main book link.
- Reference edition
- Numbered edition unspecified; binding unverified
- Format
- Not established
- Reference edition date
- 2024
- ISBN-13
- 9781638283430
Publisher description and contents reviewed. Sources checked . Book not read in full.
Access: See the publisher or author for available formats and prices. Public samples are linked where checked.
Public 52-page sample inspected, not the complete monograph. Statistical prerequisites apply; binding format remains unverified.
Applications, priorities and coverage depth are editorial judgments. Book not read in full.
Source facts and reading prompts prepared with AI assistance. The named authors and linked publishers retain credit for the original work. Source verification is separate from an assessment of current platform tactics.
Take the idea off the page.
What would make the proposed causal estimate misleading even if the model fits well?
Talk with the collective
