By Clara Bicalho, Sisi Huang, and Markus Konrad
Declare and Diagnose Your Research Design
DeclareDesign is a set of software tools for describing, assessing, and conducting empirical research.
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Model + Inquiry + Data Strategy + Answer Strategy
Research designs have four components. Learn more about the MIDA framework.
LEARN MORESoftware
You can use our software to:
- Fabricate mock data with fabricatr
- Conduct random sampling and assignment with randomizr
- Estimate effects with OLS and IV with estimatr
- Declare and diagnose research design with DeclareDesign
- Draw on a library of design templates with DesignLibrary
- Explore design tradeoffs interactively with DesignWizard
Design Library
To make getting started easy, we provide a library of common research designs. These design are observational and experimental, causal and descriptive, quantitative and qualitative.
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This guide will help you get declaring and diagnosing today.
GETTING STARTEDSee our approach in action:
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Now there is a web interface for declaring and diagnosing research designs
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An instrument does not have to be exogenous to be consistent
We often think of an instrumental variable (\(Z\)) as a random shock that generates exogenous variation in a treatment of interest \(X\). The randomness of \(Z\) lets us identify the effect of \(X\) on \(Y\), at least for units for which \(Z\) perturbs \(X\) in a way that’s not possible by just looking at the relationship between \(X\) and \(Y\). But surprisingly, we think, if effects are constant the instrumental variables estimator can be consistent for the effect of \(X\) on \(Y\) even when the relationship between the instrument (\(Z\)) and the endogenous variable (\(X\)) is confounded (for example, Hernán and Robins (2006)). That’s the good news. Less good news is that when there is effect heterogeneity you can get good estimates for some units but it can be hard to know which units those are (Swanson and Hernán 2017). We use a declaration and diagnosis to illustrate these insights.
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Some designs have badly posed questions and design diagnosis can alert you to the problem
An obvious requirement of a good research design is that the question it seeks to answer does in fact have an answer, at least under plausible models of the world. But we can sometimes get quite far along a research path without being conscious that the questions we ask do not have answers and the answers we get are answering different questions.