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Experimental Designs

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An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...

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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

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Design-Based Uncertainty for Quasi-Experiments.

Ashesh Rambachan¹, Jonathan Roth²

¹Department of Economics, Massachusetts Institute of Technology, Cambridge, MA.

Journal of the American Statistical Association

|September 29, 2025

Summary

This summary is machine-generated.

This study introduces a new design-based framework for causal inference in social science quasi-experiments. It addresses selection bias by allowing varied treatment probabilities, offering a robust foundation for empirical research.

Keywords:

Causal inference Finite population Randomization inference Selection into treatment

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Area of Science:

Social Sciences
Statistics
Causal Inference

Background:

Design-based frameworks are common for random assignment.
Quasi-experimental settings often involve concerns about unobserved selection into treatment.
Existing frameworks may not fully accommodate complex selection mechanisms.

Purpose of the Study:

To develop a design-based framework for quasi-experimental causal inference in social sciences.
To analyze settings with stochastic treatment assignment and potential selection bias.
To provide conditions for interpretable causal parameters and characterize inferential distortions.

Main Methods:

Developing a framework where treatment assignment is stochastic but units have differing probabilities.
Identifying conditions under which quasi-experimental estimators yield interpretable finite-population causal parameters.
Characterizing biases and distortions when selection conditions are violated.

Main Results:

The proposed framework accommodates rich forms of selection into treatment.
Conditions are provided for popular quasi-experimental estimators to recover interpretable causal parameters.
Biases and distortions due to selection violations are systematically characterized.

Conclusions:

The framework offers a rigorous foundation for quasi-experimental analyses in social sciences.
Results facilitate sensitivity analyses for concerns about selection bias.
The approach better aligns with empirical researchers' discussions of data variation.