causal-inference

To estimate treatment effects with difference-in-differences (DiD) under staggered adoption, you implement the design using observational data while checking for parallel pre-trends and running robustness diagnostics. The framework provides practical examples and code snippets for this exact quasi-experimental setup.

Identifying causal effects from observational data requires outlining methodological frameworks and identification arguments. You choose from strategies like IV/2SLS, DiD, RDD, synthetic control, or matching, then apply diagnostics and robustness checks to support reproducible analysis.

Yes, you can run causal inference diagnostics such as overidentification and pre-trends tests in both Python and R. The framework provides practical guidance with definitions, examples, and code snippets across both languages to support reproducible analysis.

You need synthetic control when estimating treatment effects for a single treated unit by creating a weighted synthetic counterfactual, whereas regression discontinuity design (RDD) is used when treatment assignment is determined by a threshold cutoff. Both are supported quasi-experimental identification strategies.

To set up an instrumental variables (IV/2SLS) estimation strategy, you provide a causal research question and observational data, select IV/2SLS as your identification approach, and run overidentification tests to ensure instrument validity within your reproducible analysis workflow.