Add practitioner decision tree and getting started guide (B1b-d)

README.md

@@ -75,6 +75,16 @@ After estimation, call `practitioner_next_steps(results)` for context-aware guid
 
 Detailed guide: [`docs/llms-practitioner.txt`](docs/llms-practitioner.txt)
 
+## For Data Scientists
+
+Measuring campaign lift? Evaluating a product launch? diff-diff handles the causal inference so you can focus on the business question.
+
+- **[Which method fits my problem?](docs/practitioner_decision_tree.rst)** - Start from your business scenario (campaign in some markets, staggered rollout, survey data) and find the right estimator
+- **[Getting started for practitioners](docs/practitioner_getting_started.rst)** - End-to-end walkthrough: marketing campaign -> causal estimate -> stakeholder-ready result
+- **[Brand awareness survey tutorial](docs/tutorials/17_brand_awareness_survey.ipynb)** - Full example with complex survey design, brand funnel analysis, and staggered rollouts
+
+Already know DiD? The [academic quickstart](docs/quickstart.rst) and [estimator guide](docs/choosing_estimator.rst) cover the full technical details.
+
 ## Features
 
 - **sklearn-like API**: Familiar `fit()` interface with `get_params()` and `set_params()`

ROADMAP.md

@@ -76,9 +76,9 @@ Parallel track targeting data science practitioners — marketing, product, oper
 | Item | Priority | Status |
 |------|----------|--------|
 | **B1a.** Brand Awareness Survey DiD tutorial — lead use case showcasing unique survey support | HIGH | Done (Tutorial 17) |
-| **B1b.** README "For Data Scientists" section alongside "For Academics" and "For AI Agents" | HIGH | Not started |
-| **B1c.** Practitioner decision tree — "which method should I use?" framed for business contexts | HIGH | Not started |
-| **B1d.** "Getting Started" guide for practitioners with business ↔ academic terminology bridge | MEDIUM | Not started |
+| **B1b.** README "For Data Scientists" section alongside "For Academics" and "For AI Agents" | HIGH | Done |
+| **B1c.** Practitioner decision tree — "which method should I use?" framed for business contexts | HIGH | Done |
+| **B1d.** "Getting Started" guide for practitioners with business ↔ academic terminology bridge | MEDIUM | Done |
 
 ### Phase B2: Practitioner Content
 

docs/doc-deps.yaml

@@ -101,6 +101,10 @@ sources:
         type: user_guide
       - path: docs/benchmarks.rst
         type: performance
+      - path: docs/practitioner_getting_started.rst
+        type: user_guide
+      - path: docs/practitioner_decision_tree.rst
+        type: user_guide
 
   diff_diff/twfe.py:
     drift_risk: low
@@ -141,6 +145,12 @@ sources:
       - path: docs/survey-roadmap.md
         type: roadmap
         note: "Phase 4/6/7 CS survey sections"
+      - path: docs/practitioner_getting_started.rst
+        section: "What If Your Campaign Rolled Out in Waves?"
+        type: user_guide
+      - path: docs/practitioner_decision_tree.rst
+        section: "Staggered Rollout"
+        type: user_guide
 
   # ── StaggeredTripleDifference (staggered_triple_diff group) ─────────
 
@@ -265,6 +275,9 @@ sources:
         type: user_guide
       - path: docs/choosing_estimator.rst
         type: user_guide
+      - path: docs/practitioner_decision_tree.rst
+        section: "Varying Spending Levels"
+        type: user_guide
 
   # ── SyntheticDiD ───────────��───────────────────────────────────────
 
@@ -284,6 +297,9 @@ sources:
       - path: docs/llms-full.txt
         section: "SyntheticDiD"
         type: user_guide
+      - path: docs/practitioner_decision_tree.rst
+        section: "Few Test Markets"
+        type: user_guide
       - path: docs/choosing_estimator.rst
         type: user_guide
       - path: docs/benchmarks.rst
@@ -471,6 +487,12 @@ sources:
       - path: docs/choosing_estimator.rst
         section: "Survey Design Support"
         type: user_guide
+      - path: docs/practitioner_decision_tree.rst
+        section: "Survey Data"
+        type: user_guide
+      - path: docs/practitioner_getting_started.rst
+        section: "What If You Have Survey Data?"
+        type: user_guide
 
   # ── Infrastructure ─────────────────────────────────────────────────
 
@@ -493,6 +515,9 @@ sources:
       - path: docs/methodology/REGISTRY.md
         section: "Inference, safe_inference NaN gating"
         type: methodology
+      - path: docs/practitioner_getting_started.rst
+        section: "Step 4: Check Whether the Result Is Trustworthy"
+        type: user_guide
       - path: CLAUDE.md
         section: "safe_inference pattern"
         type: internal
@@ -531,6 +556,10 @@ sources:
     docs:
       - path: docs/api/prep.rst
         type: api_reference
+      - path: docs/practitioner_getting_started.rst
+        type: user_guide
+      - path: docs/practitioner_decision_tree.rst
+        type: user_guide
 
   diff_diff/datasets.py:
     drift_risk: low

docs/index.rst

@@ -44,13 +44,23 @@ For development:
 Quick Links
 -----------
 
+- :doc:`practitioner_getting_started` - Measuring campaign impact? Start here
+- :doc:`practitioner_decision_tree` - Which method fits your business problem?
 - :doc:`quickstart` - Installation and your first DiD analysis
 - :doc:`choosing_estimator` - Which estimator should I use?
 - :doc:`tutorials/01_basic_did` - Hands-on basic tutorial
 - :doc:`troubleshooting` - Common issues and solutions
 - :doc:`r_comparison` - Coming from R?
 - :doc:`api/index` - Full API reference
 
+.. toctree::
+   :maxdepth: 2
+   :caption: For Data Scientists
+   :hidden:
+
+   practitioner_getting_started
+   practitioner_decision_tree
+
 .. toctree::
    :maxdepth: 2
    :caption: Getting Started
@@ -60,6 +70,13 @@ Quick Links
    choosing_estimator
    troubleshooting
 
+.. toctree::
+   :maxdepth: 1
+   :caption: Tutorials: Business Applications
+   :hidden:
+
+   tutorials/17_brand_awareness_survey
+
 .. toctree::
    :maxdepth: 1
    :caption: Tutorials: Fundamentals