did_causal_analysis
Difference-in-Differences causal analysis to identify demographic drivers of behavioral changes with p-value significance testing. Use for event effects, A/B testing, or policy evaluation.
Best for Data scientists analyzing b…Works with GitHub
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@benchflow-ai- language
- PDDL
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Apr 20, 2026
✦overview.md
Key Features
- ·Difference-in-Differences causal analysis framework
- ·Automatic method selection based on sample size
- ·Handles both intensive margin (continuous outcomes) and extensive margin (binary outcomes)
- ·Multivariate heterogeneous DiD for simultaneous feature estimation
- ·Univariate DiD for separate feature regressions
- ·P-value significance testing for demographic drivers
Use Cases
- →Identifying demographic drivers of behavioral changes after events
- →Evaluating policy impacts on user behavior
- →Analyzing A/B test results with causal inference
- →Measuring adoption rates versus intensity of behavior changes
Best for
- ✓Data scientists analyzing behavioral data
- ✓Product teams evaluating feature impacts
- ✓Policy researchers assessing intervention effects
Not ideal for
- !Small datasets with limited samples
- !Non-causal descriptive analysis
- !Real-time streaming data analysis
FAQs
name
did_causal_analysis
description
Difference-in-Differences causal analysis to identify demographic drivers of behavioral changes with p-value significance testing. Use for event effects, A/B testing, or policy evaluation.
Difference-in-Differences Framework
DiD framework for causal inference with automatic method selection (Multivariate vs Univariate) based on sample size.
Methodology
Intensive Margin
- Definition: How much behavior changed among participants (continuous outcome)
- Data Structure: Sparse data - only entities that participated (made purchases, had events, etc.)
- Outcome: Continuous (e.g., spending amount per purchaser)
- Key Point: Do NOT include non-participants or create complete grids - only analyze actual participants
Extensive Margin
- Definition: How many people changed behavior (binary 0/1 outcome)
- Data Structure: Complete panel - all entities × all periods (includes non-participants)
- Outcome: Binary (0/1 indicating participation)
- Key Point: MUST include non-participants to measure adoption ratess
Regression Methods
Both methods use: Y = α + βX + γPost + δ(X × Post) + ε where δ is the DiD estimate.
Multivariate Heterogeneous DiD
- Estimates ALL features simultaneously in one regression
- Controls for confounding between features
- Used when:
n ≥ k × min_sample_ratio(default: 10k samples minimum)
$install
1-click copynpx skills add benchflow-ai/skillsbench --skill did_causal_analysisSafety assessment
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Clarity score
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4/ 5
very goodClear and well structured, with only minor parts that might need a second read.
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Actionability score
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3/ 5
mediumPartially actionable with several concrete steps, but still missing important details.