A tailored course, built for your situation
Advanced Applied Econometrics for Policy Research
A structured path to strengthen causal inference, refine model validity, and accelerate publication-ready analysis
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Struggling to translate complex data into credible, publication-ready findings?
The situation this course is for
Even with strong theoretical grounding, empirical work in policy economics often stalls due to weak instruments, unobserved heterogeneity, or model misspecification. Researchers face pressure to publish while balancing data limitations, reviewer expectations, and methodological rigor. Standard textbooks don’t address the nuances of real-world datasets or the subtle decisions that determine whether a paper gets accepted or rejected.
Who this is for
Academic econometrician focused on policy evaluation, with active research in empirical methods and survey-based analysis. Values precision, peer recognition, and methodological transparency.
Who this is not for
This is not for students seeking introductory econometrics, practitioners without research aims, or those focused solely on theoretical modeling without empirical application.
What you walk away with
- Strengthen identification strategies in observational data
- Improve robustness checks and instrument validity
- Reduce time from data collection to publication-ready tables
- Increase confidence in causal claims
- Apply advanced techniques like doubly robust estimation and sensitivity analysis
The 12 modules (with all 144 chapters)
Module 1. Foundations of Causal Inference
Establish core principles of causality, including potential outcomes, treatment effects, and the role of randomization. Clarify assumptions behind difference-in-differences, matching, and instrumental variables.
12 chapters in this module
- Potential outcomes framework
- Average treatment effect definitions
- Selection on observables vs unobservables
- Role of randomization
- Threats to internal validity
- Counterfactual reasoning basics
- Stable unit treatment value assumption
- Compliance and intent-to-treat
- Local average treatment effects
- Overlap and common support
- Balancing covariates
- SUTVA violations
Module 2. Instrumental Variables Deep Dive
Master the use of instruments in endogenous regressor models. Focus on validity tests, weak instruments, and interpretation of LATE. Includes real-data diagnostics and replication exercises.
12 chapters in this module
- Endogeneity sources
- Exclusion restriction meaning
- Relevance condition testing
- F-statistic thresholds
- Weak instrument bias correction
- Overidentification tests
- Heterogeneous treatment effects
- Multiple instruments handling
- Control function approach
- LATE interpretation
- Instrument ranking methods
- Placebo instrument testing
Module 3. Difference-in-Differences Design
Refine DiD setups with attention to parallel trends, dynamic effects, and post-treatment heterogeneity. Integrate modern robustness checks and event study plotting.
12 chapters in this module
- Parallel trends assumption
- Event study design setup
- Pre-trend testing
- Staggered adoption timing
- Two-way fixed effects critique
- Callaway & Sant'Anna method
- DID with covariates
- Placebo timing tests
- Dynamic effect visualization
- Aggregation bias avoidance
- Robust standard errors
- Synthetic control alternatives
Module 4. Matching and Propensity Scores
Implement exact, nearest neighbor, and radius matching. Emphasize balance diagnostics, caliper selection, and sensitivity to unmeasured confounding.
12 chapters in this module
- Propensity score estimation
- Logit vs machine learning
- Common support definition
- Nearest neighbor matching
- Radius matching setup
- Kernel matching options
- Balance checking methods
- Standardized mean differences
- Sensitivity to hidden bias
- Matching with replacement
- Variance adjustment
- Weighted regression after matching
Module 5. Regression Discontinuity Designs
Build sharp and fuzzy RD models with optimal bandwidth selection, continuity assumptions, and robustness across functional forms.
12 chapters in this module
- Sharp RD setup
- Fuzzy RD as IV
- Bandwidth selection rules
- McCrary density test
- Continuity of covariates
- Local linear regression
- Bias correction methods
- Placebo cutoffs
- Global polynomial pitfalls
- Robust inference
- Kink designs
- Multiple cutoff handling
Module 6. Panel Data and Fixed Effects
Leverage longitudinal structures with fixed effects, first differencing, and within transformation. Address time-invariant omitted variables.
12 chapters in this module
- Within transformation
- First differencing logic
- Fixed effects estimation
- Time dummies inclusion
- Clustered standard errors
- Unobserved heterogeneity control
- Strict exogeneity check
- Dynamic panel models
- Arellano-Bond GMM
- Weak instruments in GMM
- Hausman test interpretation
- Between effects comparison
Module 7. Survey Data and Sampling Weights
Handle complex survey designs, non-response adjustments, and weighting efficiency. Apply corrections for stratification and clustering.
12 chapters in this module
- Complex survey design
- Stratification adjustment
- Clustering effects
- Sampling weights usage
- Non-response bias
- Post-stratification
- Calibration estimators
- Design effect calculation
- Subpopulation analysis
- Weight truncation
- Variance estimation
- Survey package workflows
Module 8. Measurement Error and Validation
Diagnose and correct for classical and non-classical measurement error. Use validation subsamples and repeated measures.
12 chapters in this module
- Classical error assumption
- Attenuation bias direction
- Instrumental variables correction
- Repeated measures use
- Validation sample design
- Error-in-variables models
- Berkson error distinction
- Misclassification in dummies
- Sensitivity analysis
- Bounding approaches
- Reliability ratios
- Prevalence estimation
Module 9. Nonlinear Models and Marginal Effects
Estimate and interpret logit, probit, and count models with correct marginal effects and average partial effects.
12 chapters in this module
- Logit model structure
- Probit assumptions
- Marginal effects at means
- Average marginal effects
- Discrete change calculation
- Count model selection
- Poisson regression
- Negative binomial use
- Zero-inflated models
- Overdispersion tests
- Predicted probabilities
- Post-estimation interpretation
Module 10. Robustness and Sensitivity Analysis
Test model stability under alternative specifications, functional forms, and exclusion assumptions. Document sensitivity transparently.
12 chapters in this module
- Specification curve analysis
- Functional form tests
- Omitted variable bias
- Cook's distance
- DFBETAS influence
- Leave-one-out stability
- Placebo variables
- Subset robustness
- Bounding estimates
- Confidence intervals
- Multiple imputation checks
- Design sensitivity
Module 11. Publication-Ready Tables and Reproducibility
Format results for submission with consistent standards, automated pipelines, and open science compliance.
12 chapters in this module
- Table formatting standards
- Star notation clarity
- Regression table stacking
- Automated export tools
- LaTeX integration
- Codebook creation
- Data availability statements
- Pre-registration benefits
- Replication package setup
- Version control basics
- Peer review response prep
- Supplemental materials
Module 12. Advanced Topics in Applied Work
Integrate modern extensions like doubly robust estimators, machine learning in controls, and synthetic controls for comparative case studies.
12 chapters in this module
- Doubly robust estimation
- TMLE introduction
- Machine learning controls
- LASSO for selection
- Synthetic control method
- Placebo validation
- Ridge regression use
- Cross-validation tuning
- Causal forests
- Double ML setup
- High-dimensional inference
- Replication workflows
How this maps to your situation
- Researcher validating policy impact
- Academic preparing manuscript for submission
- Reviewer evaluating empirical robustness
- PhD candidate advancing applied chapter
Before vs. after
Before
Uncertain about which identification strategy holds under scrutiny, spending excessive time on revisions, or facing reviewer pushback on model choice.
After
Confident in methodological rigor, able to defend design choices, and producing publication-ready analysis faster with fewer iterations.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3, 4 hours per module, designed for integration into active research cycles.
If nothing changes
Continuing with suboptimal methods increases the likelihood of reviewer rejection, delays in publication, and diminished impact of otherwise strong research contributions.
How this compares to the alternatives
Unlike generic econometrics courses, this program focuses exclusively on advanced empirical challenges in policy research, with direct application to real datasets and publication standards.
Frequently asked
Is this course suitable for someone with prior econometrics training?
Yes, it's designed for researchers who already understand basics but want to deepen methodological rigor and publication readiness.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Are datasets and code included?
Yes, each module includes downloadable templates, worked examples, and implementation-ready code snippets.
$199 one-time. Approximately 3, 4 hours per module, designed for integration into active research cycles..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours