Propensity Score Analysis Made Simple

Propensity score analysis has revolutionized how researchers approach causal inference in observational studies. What once required specialized statistical software and deep programming knowledge can now be accomplished in a familiar spreadsheet environment with AI assistance.

Whether you're evaluating treatment effectiveness in healthcare research, assessing policy interventions, or analyzing marketing campaign impacts, propensity score methods help you draw meaningful causal conclusions from non-randomized data.

Understanding Propensity Score Analysis

Propensity score analysis addresses a fundamental challenge in observational research: selection bias. When subjects aren't randomly assigned to treatment and control groups, confounding variables can distort your results.

The propensity score represents the probability that a subject receives treatment, given their observed characteristics. By balancing groups based on these scores, you can simulate the conditions of a randomized experiment.

Key Components

Propensity Score Estimation: Calculate treatment probabilities using logistic regression

Matching Methods: Pair treated and control units with similar scores

Stratification: Group subjects into strata based on score ranges

Weighting: Apply inverse probability weights to balance groups

With Sourcetable's AI assistance, you can implement these methods without memorizing complex formulas or struggling with statistical syntax.

Why Choose Sourcetable for Propensity Score Analysis

Real-World Propensity Score Applications

How to Perform Propensity Score Analysis in Sourcetable

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Advanced Propensity Score Techniques

Once you've mastered basic propensity score methods, Sourcetable enables you to implement sophisticated extensions:

Doubly Robust Estimation

Combine propensity score weighting with outcome regression modeling. This approach provides unbiased estimates even if either the propensity score model or outcome model is misspecified (but not both).

Machine Learning Propensity Scores

Leverage random forests, gradient boosting, or neural networks to estimate propensity scores when relationships between covariates and treatment are complex or non-linear.

Generalized Propensity Scores

Extend analysis to continuous or multi-valued treatments. Instead of binary treatment assignment, model the probability density of receiving different treatment intensities.

Time-Varying Treatments

Handle situations where treatment status changes over time using marginal structural models and inverse probability of treatment weighting.

Propensity Score Analysis Best Practices

Model Specification Guidelines

Include True Confounders: Focus on variables that affect both treatment assignment and outcomes

Avoid Post-Treatment Variables: Don't include variables that might be affected by treatment

Consider Interactions: Include interaction terms when treatment effects vary across subgroups

Quality Assessment Criteria

Standardized Mean Differences: Target values below 0.1 for good balance

Variance Ratios: Should be between 0.5 and 2.0 for adequate balance

Overlap Assessment: Ensure adequate common support across the propensity score range

Reporting Standards

Document your propensity score methodology thoroughly. Include model specifications, balance diagnostics, sensitivity analyses, and assumptions. Sourcetable automatically generates comprehensive analysis reports following best practices.