Add ContinuousDiD to ReadTheDocs documentation

docs/api/continuous_did.rst

@@ -0,0 +1,159 @@
+Continuous Difference-in-Differences
+=====================================
+
+Continuous DiD estimator for dose-response curves with continuous treatment intensity.
+
+This module implements the methodology from Callaway, Goodman-Bacon & Sant'Anna (2024),
+"Difference-in-Differences with a Continuous Treatment" (NBER WP 32117), which:
+
+1. **Estimates dose-response curves**: ATT(d) and ACRT(d) as functions of dose
+2. **Computes summary parameters**: Overall ATT (binarized) and ACRT aggregated across doses
+3. **Uses B-spline smoothing**: Flexible nonparametric estimation of dose-response functions
+4. **Supports multiplier bootstrap**: Valid inference with proper standard errors and CIs
+
+.. note::
+
+   **Identification assumptions.** The dose-response curves ATT(d) and ACRT(d),
+   as well as ATT\ :sup:`glob` and ACRT\ :sup:`glob`, require the **Strong Parallel
+   Trends (SPT)** assumption — that there is no selection into dose groups based on
+   treatment effects. Under the weaker standard Parallel Trends (PT) assumption,
+   only the binarized ATT\ :sup:`loc` (``overall_att``) is identified; it equals
+   ATT\ :sup:`glob` only when SPT holds. See Callaway, Goodman-Bacon & Sant'Anna
+   (2024), Assumptions 1–2.
+
+**When to use Continuous DiD:**
+
+- Treatment varies in **intensity or dose** across units (not just binary on/off)
+- You want to estimate how effects change with treatment dose
+- Staggered adoption with heterogeneous dose levels
+- You need the full dose-response curve, not just a single average effect
+
+**Data requirements:**
+
+- An **untreated group** (D = 0) must be present in the data
+- A **balanced panel** is required (all units observed in all time periods)
+- **Dose must be time-invariant** — each unit's dose is fixed across periods
+
+**Reference:** Callaway, B., Goodman-Bacon, A., & Sant'Anna, P. H. C. (2024).
+Difference-in-Differences with a Continuous Treatment. *NBER Working Paper* 32117.
+`<https://www.nber.org/papers/w32117>`_
+
+.. module:: diff_diff.continuous_did
+
+ContinuousDiD
+--------------
+
+Main estimator class for Continuous Difference-in-Differences.
+
+.. autoclass:: diff_diff.ContinuousDiD
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :inherited-members:
+
+   .. rubric:: Methods
+
+   .. autosummary::
+
+      ~ContinuousDiD.fit
+      ~ContinuousDiD.get_params
+      ~ContinuousDiD.set_params
+
+ContinuousDiDResults
+--------------------
+
+Results container for Continuous DiD estimation.
+
+.. autoclass:: diff_diff.continuous_did_results.ContinuousDiDResults
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+   .. rubric:: Methods
+
+   .. autosummary::
+
+      ~ContinuousDiDResults.summary
+      ~ContinuousDiDResults.print_summary
+      ~ContinuousDiDResults.to_dataframe
+
+DoseResponseCurve
+-----------------
+
+Dose-response curve container for ATT(d) or ACRT(d).
+
+.. autoclass:: diff_diff.continuous_did_results.DoseResponseCurve
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+   .. rubric:: Methods
+
+   .. autosummary::
+
+      ~DoseResponseCurve.to_dataframe
+
+Example Usage
+-------------
+
+Basic usage::
+
+    from diff_diff import ContinuousDiD, generate_continuous_did_data
+
+    data = generate_continuous_did_data(n_units=200, seed=42)
+
+    est = ContinuousDiD(n_bootstrap=199, seed=42)
+    results = est.fit(data, outcome='outcome', unit='unit',
+                      time='period', first_treat='first_treat',
+                      dose='dose', aggregate='dose')
+    results.print_summary()
+
+Accessing dose-response curves::
+
+    # ATT(d) dose-response curve as DataFrame
+    att_df = results.dose_response_att.to_dataframe()
+    print(att_df[['dose', 'effect', 'se', 'p_value']])
+
+    # ACRT(d) dose-response curve
+    acrt_df = results.dose_response_acrt.to_dataframe()
+
+    # Overall summary parameters
+    print(f"Overall ATT: {results.overall_att:.3f} (SE: {results.overall_att_se:.3f})")
+    print(f"Overall ACRT: {results.overall_acrt:.3f} (SE: {results.overall_acrt_se:.3f})")
+
+Event study aggregation::
+
+    # Dynamic effects (binarized ATT by relative period)
+    results_es = est.fit(data, outcome='outcome', unit='unit',
+                         time='period', first_treat='first_treat',
+                         dose='dose', aggregate='eventstudy')
+    es_df = results_es.to_dataframe(level='event_study')
+
+Comparison with CallawaySantAnna
+--------------------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 20 40 40
+
+   * - Feature
+     - ContinuousDiD
+     - CallawaySantAnna
+   * - Treatment type
+     - Continuous dose / intensity
+     - Binary (treated / not treated)
+   * - Target parameter
+     - ATT\ :sup:`loc` (PT); ATT(d), ACRT(d), ATT\ :sup:`glob`, ACRT\ :sup:`glob` (SPT)
+     - ATT(g,t), aggregated ATT
+   * - Smoothing
+     - B-spline basis for dose-response
+     - None (nonparametric group-time)
+   * - Dose-response curve
+     - Yes (full curve with CIs)
+     - No
+   * - Bootstrap
+     - Multiplier bootstrap (optional)
+     - Multiplier bootstrap (optional)
+   * - Control group
+     - never_treated / not_yet_treated
+     - never_treated / not_yet_treated

docs/api/index.rst

@@ -22,6 +22,7 @@ Core estimator classes for DiD analysis:
    diff_diff.StackedDiD
    diff_diff.TripleDifference
    diff_diff.TROP
+   diff_diff.ContinuousDiD
 
 Results Classes
 ---------------
@@ -46,6 +47,8 @@ Result containers returned by estimators:
    diff_diff.TripleDifferenceResults
    diff_diff.StackedDiDResults
    diff_diff.trop.TROPResults
+   diff_diff.ContinuousDiDResults
+   diff_diff.DoseResponseCurve
 
 Visualization
 -------------
@@ -166,6 +169,7 @@ Utilities for preparing DiD data:
    :nosignatures:
 
    diff_diff.generate_did_data
+   diff_diff.generate_continuous_did_data
    diff_diff.make_treatment_indicator
    diff_diff.make_post_indicator
    diff_diff.wide_to_long
@@ -190,6 +194,7 @@ Detailed documentation by module:
    stacked_did
    triple_diff
    trop
+   continuous_did
    results
    visualization
    diagnostics

docs/choosing_estimator.rst

@@ -8,6 +8,11 @@ Decision Flowchart
 
 Start here and follow the questions:
 
+0. **Is treatment continuous?** (Units receive different doses or intensities)
+
+   - **No** → Go to question 1
+   - **Yes** → Use :class:`~diff_diff.ContinuousDiD`
+
 1. **Is treatment staggered?** (Different units treated at different times)
 
    - **No** → Go to question 2
@@ -58,6 +63,10 @@ Quick Reference
      - Few treated units, many controls
      - Synthetic parallel trends
      - ATT with unit/time weights
+   * - ``ContinuousDiD``
+     - Continuous dose / treatment intensity
+     - Strong Parallel Trends (SPT) for dose-response; PT for binarized ATT
+     - ATT\ :sup:`loc` (PT); ATT(d), ACRT(d) (SPT)
 
 Detailed Guidance
 -----------------
@@ -173,6 +182,38 @@ Use :class:`~diff_diff.SyntheticDiD` when:
    # View the unit weights
    print(results.unit_weights)
 
+Continuous Treatment
+~~~~~~~~~~~~~~~~~~~~
+
+Use :class:`~diff_diff.ContinuousDiD` when:
+
+- Treatment varies in **intensity or dose** (e.g., subsidy amount, hours of training)
+- You want to estimate how effects change with treatment dose
+- You need the full dose-response curve, not just a single average effect
+- Staggered adoption where units receive different treatment levels
+
+.. note::
+
+   Dose-response curves ATT(d) and ACRT(d) require **Strong Parallel Trends (SPT)**.
+   Under standard PT only the binarized ATT\ :sup:`loc` is identified.
+   Data must include an untreated group (D = 0), a balanced panel, and
+   time-invariant dose (each unit's dose is fixed across periods).
+
+.. code-block:: python
+
+   from diff_diff import ContinuousDiD, generate_continuous_did_data
+
+   data = generate_continuous_did_data(n_units=200, seed=42)
+
+   est = ContinuousDiD(n_bootstrap=199, seed=42)
+   results = est.fit(data, outcome='outcome', unit='unit',
+                     time='period', first_treat='first_treat',
+                     dose='dose', aggregate='dose')
+
+   # Overall effect and dose-response curve
+   print(f"Overall ATT: {results.overall_att:.3f}")
+   att_curve = results.dose_response_att.to_dataframe()
+
 Common Pitfalls
 ---------------
 
@@ -234,6 +275,9 @@ differences helps interpret results and choose appropriate inference.
    * - ``SyntheticDiD``
      - Bootstrap or placebo-based
      - Default uses bootstrap resampling. Set ``n_bootstrap=0`` for placebo-based inference using pre-treatment residuals.
+   * - ``ContinuousDiD``
+     - Analytical (default)
+     - Uses delta method SEs by default. Use ``n_bootstrap=199`` (or higher) for multiplier bootstrap inference with proper CIs.
 
 **Recommendations by sample size:**