From Research to ROI: New Method: Doubly Robust Estimation - Combining Strengths
- Maria Alice Maia
- Nov 18, 2024
- 2 min read
Your team needs to know if the new digital invoicing system is truly making them more efficient.
You have two standard ways to measure its causal impact:
Outcome Regression: Model the outcome (e.g., time-to-close-books) based on the new system and other factors like team size and client complexity.
Propensity Score Weighting: Model the treatment (i.e., which teams adopted the new system) and use those probabilities to re-weight the data to remove selection bias.
Which path do you choose? Which model do you bet your multi-million dollar decision on?
This is a high-stakes gamble, because both methods share a single, critical point of failure: they only give you an unbiased answer if your model is correctly specified. If you get the model wrong—even slightly—your result is biased.
This is a classic "Doing Data Wrong" scenario where you're forced to make one bet. But what if there was a way to get two chances to be right?
This is the power of a more advanced technique from the causal inference playbook: Doubly Robust Estimation.
Think of it as building an analytical strategy with a backup generator. A doubly robust estimator, like the widely used Augmented Inverse Probability Weighting (AIPW) estimator, cleverly combines both an outcome model and a propensity score model.
And here's the magic: you get a consistent, unbiased estimate of the causal effect if
EITHER your outcome model is correct OR your propensity score model is correct. You don't need both to be perfect.
How this plays out for an Accounting & Finance Department:
The Challenge: An accounting firm wants to measure if a new invoicing software reduces the time it takes to close the monthly books. Adoption isn't random; more tech-savvy teams adopt it first.
The Single-Bet Mistake: They could build an outcome model, but they might miss how the software's benefit changes with client complexity (misspecification!). Or, they could model the propensity to adopt, but miss a key reason why certain partners pushed for it (misspecification!). Either mistake taints the result.
The Doubly Robust Solution: By using a doubly robust estimator, the firm's analysts get a reliable answer even if their model of how the software improves efficiency is imperfect, as long as their model of which teams adopted it is solid (or vice-versa). This "double guarantee" provides a safeguard against modeling errors, leading to a much more credible and trustworthy ROI calculation.
My career, from building startups to leading strategy at firms like FALCONI, has been focused on building resilient systems. Doubly robust estimation applies that same engineering principle to data analysis. It's about acknowledging the reality that our models are imperfect and building in a safety net.
My mission is to translate these powerful, professional-grade methods from the academic frontier into your team's playbook. This knowledge isn't mine to keep.
If you’re ready to move beyond single-bet analyses and build more robust, defensible insights, join my movement. Subscribe to my email list.
And if you’re facing a high-stakes measurement problem, book a 20-minute, no-nonsense consultation with me. Let’s discuss how to give your analysis a second chance to be right.
