ReddRadar
Agent skill
causal-inference-engine
Causal inference skill for estimating treatment effects and understanding causal relationships in business data
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/causal-inference-engine
Metadata
Additional technical details for this skill
- domain
- business
- category
- forecasting
- priority
- medium
- specialization
- decision-intelligence
- tools libraries
-
[ "econml", "dowhy", "causalml", "statsmodels" ] - shared candidate
- YES
SKILL.md
Causal Inference Engine
Overview
The Causal Inference Engine skill provides sophisticated methods for estimating causal effects from observational data. It enables business analysts to move beyond correlation to understand true cause-and-effect relationships, supporting evidence-based decision-making for interventions, policy changes, and strategic initiatives.
Capabilities
- Propensity score matching
- Inverse probability weighting
- Difference-in-differences
- Instrumental variables
- Regression discontinuity
- Synthetic control methods
- Causal forest implementation
- Sensitivity analysis to unobserved confounding
Used By Processes
- A/B Testing and Experimentation Framework
- Predictive Analytics Implementation
- Win/Loss Analysis Program
Usage
Problem Definition
# Define causal question
causal_problem = {
"treatment": "marketing_campaign",
"outcome": "purchase_conversion",
"confounders": ["customer_segment", "prior_purchases", "channel", "region"],
"instruments": ["random_assignment_probability"], # if available
"effect_type": "ATE", # Average Treatment Effect
"heterogeneity": ["customer_segment", "tenure"] # for CATE
}
Propensity Score Matching
# Propensity score configuration
psm_config = {
"method": "propensity_score_matching",
"estimator": "logistic_regression",
"matching": {
"method": "nearest_neighbor",
"caliper": 0.1,
"replacement": False,
"ratio": 1
},
"balance_check": True,
"covariates": ["age", "income", "prior_purchases", "engagement_score"]
}
Difference-in-Differences
# DiD configuration
did_config = {
"method": "difference_in_differences",
"treatment_group": "stores_with_intervention",
"control_group": "stores_without_intervention",
"pre_period": ["2023-01", "2023-06"],
"post_period": ["2023-07", "2023-12"],
"parallel_trends_test": True,
"fixed_effects": ["store_id", "month"]
}
Causal Forest (Heterogeneous Effects)
# Causal forest for CATE
causal_forest_config = {
"method": "causal_forest",
"n_trees": 1000,
"honest": True,
"effect_modifiers": ["customer_segment", "tenure", "region"],
"output": {
"individual_effects": True,
"confidence_intervals": True,
"variable_importance": True
}
}
Method Selection Guide
| Method | When to Use | Assumptions |
|---|---|---|
| Propensity Score | Selection on observables | No unmeasured confounding |
| Difference-in-Differences | Pre/post with control group | Parallel trends |
| Regression Discontinuity | Threshold-based treatment | Continuity at threshold |
| Instrumental Variables | Unmeasured confounding exists | Valid instrument |
| Synthetic Control | Aggregate-level intervention | Pre-treatment fit |
| Causal Forest | Heterogeneous effects | Unconfoundedness |
Input Schema
{
"causal_problem": {
"treatment": "string",
"outcome": "string",
"confounders": ["string"],
"effect_type": "ATE|ATT|CATE"
},
"data": "dataframe or path",
"method_config": {
"method": "string",
"parameters": "object"
},
"validation": {
"refutation_tests": ["placebo", "subset", "random_common_cause"],
"sensitivity_analysis": "boolean"
}
}
Output Schema
{
"effect_estimate": {
"point_estimate": "number",
"confidence_interval": ["number", "number"],
"p_value": "number",
"standard_error": "number"
},
"heterogeneous_effects": {
"subgroup": {
"effect": "number",
"ci": ["number", "number"]
}
},
"diagnostics": {
"balance_statistics": "object",
"parallel_trends_test": "object",
"first_stage_f_stat": "number (IV)"
},
"refutation_results": {
"test_name": {
"original_effect": "number",
"refuted_effect": "number",
"passed": "boolean"
}
},
"sensitivity": {
"robustness_value": "number",
"interpretation": "string"
}
}
Best Practices
- Clearly articulate the causal question before analysis
- Draw a causal diagram (DAG) to identify confounders
- Check covariate balance after matching/weighting
- Perform sensitivity analysis to unmeasured confounding
- Use multiple refutation tests to validate results
- Report effect sizes with confidence intervals
- Be transparent about assumptions and limitations
Refutation Tests
| Test | What It Checks |
|---|---|
| Placebo Treatment | Effect should be zero with random treatment |
| Placebo Outcome | Effect should be zero with unrelated outcome |
| Subset Validation | Effect should hold in subsamples |
| Random Common Cause | Adding random confounder shouldn't change effect |
Integration Points
- Feeds into Hypothesis Tracker for test results
- Connects with Experimentation Manager agent
- Supports Predictive Analyst for causal features
- Integrates with Bayesian Network Analyzer for causal graphs
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