hypothesis-building

Name: hypothesis-building
Author: scdenney

34stars0forks

How to install hypothesis-building?

scdenney/open-science-skills/hypothesis-building

$npx -y skills add scdenney/open-science-skills --skill hypothesis-building

Installs into the current project.

Summary

Build falsifiable causal hypotheses. Use for DAGs, FPCI, equivalence testing, mechanism specification, and deriving testable predictions from theory.

Files · 1

View on GitHub

SKILL.md

1	# Causal Hypothesis Architect
2
3	## Instructions
4
5	### 1. The Identification Challenge
6	- Verify FPCI Resolution: Confirm that random assignment (or the identification strategy) solves the Fundamental Problem of Causal Inference for this design (Druckman 2022).
7	- 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).
8	- 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?
9	- Map the Causal Diagram: Where appropriate, draw a DAG. Identify backdoor paths and confirm whether randomization closes them (Mutz 2011).
10	- 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).
11	- 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 a representative sample estimates the PATE directly by design, without requiring statistical modeling or extrapolation (Mutz 2011; Druckman 2022).
12
13	### 2. Hypothesis Formulation
14	- Popperian Falsifiability: Frame the hypothesis as a "basic statement", or a specific observation that, if found to be false, would invalidate the theory.
15	- The Counterfactual Logic: Every hypothesis must specify a comparison. Define the "untreated" world. If the hypothesis is that X causes Y, what is the specific state of the world where X is absent? Note that in many survey experiments there may be no "pure control" -- each condition provides information, just different information (Druckman 2022). Distinguish between active control groups (which receive different information on the same topic, controlling for the act of receiving information) and passive control groups (no information), as this choice defines the counterfactual and thus the estimand (Stantcheva 2023).
16	- Directional Clarity: Avoid "existence" claims (e.g., "there is an effect"). Use "ordinal" claims that specify the direction (higher/lower) and, where possible, the expected functional form.
17	- Beat Credible Competitors: The goal of a hypothesis test is not merely to reject the null of "no effect" but to beat credible alternative explanations. Design experiments that adjudicate between competing theories -- "the point of the experiment is explication, not demonstration" (Sniderman 2018). A hypothesis is stronger when it specifies which competing theoretical account would be undermined by the predicted result.
18	- Null-by-Design Thinking: If the theory predicts no effect below a threshold of treatment intensity, specify (a) the intensity threshold, (b) expert-panel review of treatment strength before fielding, and (c) the equivalence bounds for the statistical test. Route such designs to the equivalence test in §3 (Sniderman 2018).
19	- Estimand Specification: Every hypothesis must map to a specific estimand -- the statistical quantity that, if estimated, would test the hypothesis. State the theoretical estimand (the target quantity, defined outside any statistical model) before choosing the empirical estimand (a function of observable data) and the estimation strategy; each step requires different assumptions and should be argued separately (Lundberg, Johnson, & Stewart 2021). For experimental designs, this typically means specifying: (a) the treatment contrast (what is compared to what), (b) the outcome metric (probability, scale score, etc.), and (c) the model that produces the estimate (e.g., AMCE from a conjoint, ATE from a vignette experiment). A hypothesis without a named estimand is not pre-registrable. Where feasible, declare the design formally using the MIDA framework -- model, inquiry, data strategy, answer strategy -- so that power, bias, and estimator--estimand coherence can be diagnosed computationally before fielding (Blair, Cooper, Coppock, & Humphreys 2019). For information/pedagogical experiments, distinguish between first-stage estimands (the belief or knowledge the treatment shifts) and second-stage estimands (the policy views influenced by those beliefs), with the causal chain explicit (Stantcheva 2023).
20	- Information Equivalence: In survey e

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hypothesis-building

34stars0forks

View on GitHub

How to install hypothesis-building?

scdenney/open-science-skills/hypothesis-building

$npx -y skills add scdenney/open-science-skills --skill hypothesis-building

Installs into the current project.

Summary

Build falsifiable causal hypotheses. Use for DAGs, FPCI, equivalence testing, mechanism specification, and deriving testable predictions from theory.

Files · 1

View on GitHub

SKILL.md

1	# Causal Hypothesis Architect
2
3	## Instructions
4
5	### 1. The Identification Challenge
6	- Verify FPCI Resolution: Confirm that random assignment (or the identification strategy) solves the Fundamental Problem of Causal Inference for this design (Druckman 2022).
7	- 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).
8	- 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?
9	- Map the Causal Diagram: Where appropriate, draw a DAG. Identify backdoor paths and confirm whether randomization closes them (Mutz 2011).
10	- 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).
11	- 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 a representative sample estimates the PATE directly by design, without requiring statistical modeling or extrapolation (Mutz 2011; Druckman 2022).
12
13	### 2. Hypothesis Formulation
14	- Popperian Falsifiability: Frame the hypothesis as a "basic statement", or a specific observation that, if found to be false, would invalidate the theory.
15	- The Counterfactual Logic: Every hypothesis must specify a comparison. Define the "untreated" world. If the hypothesis is that X causes Y, what is the specific state of the world where X is absent? Note that in many survey experiments there may be no "pure control" -- each condition provides information, just different information (Druckman 2022). Distinguish between active control groups (which receive different information on the same topic, controlling for the act of receiving information) and passive control groups (no information), as this choice defines the counterfactual and thus the estimand (Stantcheva 2023).
16	- Directional Clarity: Avoid "existence" claims (e.g., "there is an effect"). Use "ordinal" claims that specify the direction (higher/lower) and, where possible, the expected functional form.
17	- Beat Credible Competitors: The goal of a hypothesis test is not merely to reject the null of "no effect" but to beat credible alternative explanations. Design experiments that adjudicate between competing theories -- "the point of the experiment is explication, not demonstration" (Sniderman 2018). A hypothesis is stronger when it specifies which competing theoretical account would be undermined by the predicted result.
18	- Null-by-Design Thinking: If the theory predicts no effect below a threshold of treatment intensity, specify (a) the intensity threshold, (b) expert-panel review of treatment strength before fielding, and (c) the equivalence bounds for the statistical test. Route such designs to the equivalence test in §3 (Sniderman 2018).
19	- Estimand Specification: Every hypothesis must map to a specific estimand -- the statistical quantity that, if estimated, would test the hypothesis. State the theoretical estimand (the target quantity, defined outside any statistical model) before choosing the empirical estimand (a function of observable data) and the estimation strategy; each step requires different assumptions and should be argued separately (Lundberg, Johnson, & Stewart 2021). For experimental designs, this typically means specifying: (a) the treatment contrast (what is compared to what), (b) the outcome metric (probability, scale score, etc.), and (c) the model that produces the estimate (e.g., AMCE from a conjoint, ATE from a vignette experiment). A hypothesis without a named estimand is not pre-registrable. Where feasible, declare the design formally using the MIDA framework -- model, inquiry, data strategy, answer strategy -- so that power, bias, and estimator--estimand coherence can be diagnosed computationally before fielding (Blair, Cooper, Coppock, & Humphreys 2019). For information/pedagogical experiments, distinguish between first-stage estimands (the belief or knowledge the treatment shifts) and second-stage estimands (the policy views influenced by those beliefs), with the causal chain explicit (Stantcheva 2023).
20	- Information Equivalence: In survey e

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6 picks

Type