hypothesis-buildinglisted

Turns a theory into falsifiable, pre-registerable hypotheses — DAGs and backdoor closure, how the design resolves the FPCI, SATE versus PATE, counterfactual and directional framing, a named theoretical and empirical estimand, a justified SESOI, the choice among NHST, interval, equivalence (TOST), and minimum-effect tests, scope conditions, and primary, secondary, and exploratory tiers across multi-experiment designs. Use when the user has a research question or theory and asks to turn it into hypotheses, asks whether a prediction is falsifiable or testable, asks what the estimand is, or asks how to predict a null. Framing the paper around it goes to narrative-building, the plan to pre-registration-writing.
scdenney/open-science-skills · ★ 57 · Web & Frontend · score 74

Install: claude install-skill scdenney/open-science-skills

# Causal Hypothesis Architect ## Instructions ### 1. The Identification Challenge - **Verify FPCI Resolution:** Confirm that random assignment (or the identification strategy) solves the Fundamental Problem of Causal Inference for this design (Druckman 2022). - **Three Prerequisites for Experiments:** Before proceeding, verify that the design meets the three prerequisites for causal inference from experiments: (1) *random assignment* to conditions, (2) *exclusion restriction* (the only difference between conditions is the treatment itself), and (3) *SUTVA* (the Stable Unit Treatment Value Assumption, i.e. noninterference -- one subject's treatment does not affect another's outcome) (Druckman 2022, following Gerber & Green 2012). - **Define the Data Generating Process (DGP):** Before drafting the hypothesis, describe the set of rules that governs how the data is created. What are the underlying mechanics of the world being studied? - **Map the Causal Diagram:** Where appropriate, draw a DAG. Identify backdoor paths and confirm whether randomization closes them (Mutz 2011). - **Close the Backdoors:** State which variables must be controlled for to isolate the treatment effect. If using an experiment, explain how random assignment closes these paths (Mutz 2011). - **SATE vs. PATE:** Distinguish between the Sample Average Treatment Effect (SATE) and the Population Average Treatment Effect (PATE). A convenience-sample experiment estimates a SATE; a population-based experiment on