Release v2.1.8

CHANGELOG.md

@@ -5,6 +5,31 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [2.1.8] - 2026-01-25
+
+### Added
+- **`/push-pr-update` skill** for committing and pushing PR revisions
+  - Commits local changes to current branch and pushes to remote
+  - Triggers AI code review automatically
+  - Robust handling for fork repos, unpushed commits, and upstream tracking
+
+### Fixed
+- **TROP estimator methodology alignment** (PR #110)
+  - Aligned with paper methodology (Equation 5, D matrix semantics)
+  - NaN propagation and LOOCV warnings improvements
+  - Rust backend test alignment with new loocv_grid_search return signature
+  - LOOCV cycling, D matrix validation fixes
+  - Final estimation infinity handling and edge case fixes
+  - Absorbing-state gap detection and n_post_periods fix
+
+### Changed
+- **`/submit-pr` skill improvements** (PR #111)
+  - Case-insensitive secret scanning with POSIX ERE regex
+  - Verify origin ref exists before push
+  - Dynamic default branch detection with fallback
+  - Robust handling for unpushed commits, fork repos
+  - Files count display in PR summary
+
 ## [2.1.7] - 2026-01-25
 
 ### Fixed
@@ -542,6 +567,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - `to_dict()` and `to_dataframe()` export methods
   - `is_significant` and `significance_stars` properties
 
+[2.1.8]: https://github.com/igerber/diff-diff/compare/v2.1.7...v2.1.8
 [2.1.7]: https://github.com/igerber/diff-diff/compare/v2.1.6...v2.1.7
 [2.1.6]: https://github.com/igerber/diff-diff/compare/v2.1.5...v2.1.6
 [2.1.5]: https://github.com/igerber/diff-diff/compare/v2.1.4...v2.1.5

diff_diff/__init__.py

@@ -136,7 +136,7 @@
     load_mpdta,
 )
 
-__version__ = "2.1.7"
+__version__ = "2.1.8"
 __all__ = [
     # Estimators
     "DifferenceInDifferences",

docs/tutorials/10_trop.ipynb

@@ -161,7 +161,33 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "# Fit TROP with automatic tuning via LOOCV\ntrop_est = TROP(\n    lambda_time_grid=[0.0, 1.0],   # Reduced time decay grid\n    lambda_unit_grid=[0.0, 1.0],   # Reduced unit distance grid  \n    lambda_nn_grid=[0.0, 0.1],     # Reduced nuclear norm grid\n    n_bootstrap=50,    # Reduced bootstrap replications for SE\n    seed=42\n)\n\n# Note: TROP infers treatment periods from the treatment indicator column.\n# The 'treated' column should be an absorbing state (D=1 for all periods\n# during and after treatment starts).\n\n# For SDID comparison later, we keep post_periods for SDID\npost_periods = list(range(n_pre, n_pre + n_post))\n\nresults = trop_est.fit(\n    df,\n    outcome='outcome',\n    treatment='treated',\n    unit='unit',\n    time='period'\n)\n\nprint(results.summary())"
+   "source": [
+    "# Fit TROP with automatic tuning via LOOCV\n",
+    "trop_est = TROP(\n",
+    "    lambda_time_grid=[0.0, 1.0],   # Reduced time decay grid\n",
+    "    lambda_unit_grid=[0.0, 1.0],   # Reduced unit distance grid  \n",
+    "    lambda_nn_grid=[0.0, 0.1],     # Reduced nuclear norm grid\n",
+    "    n_bootstrap=50,    # Reduced bootstrap replications for SE\n",
+    "    seed=42\n",
+    ")\n",
+    "\n",
+    "# Note: TROP infers treatment periods from the treatment indicator column.\n",
+    "# The 'treated' column should be an absorbing state (D=1 for all periods\n",
+    "# during and after treatment starts).\n",
+    "\n",
+    "# For SDID comparison later, we keep post_periods for SDID\n",
+    "post_periods = list(range(n_pre, n_pre + n_post))\n",
+    "\n",
+    "results = trop_est.fit(\n",
+    "    df,\n",
+    "    outcome='outcome',\n",
+    "    treatment='treated',\n",
+    "    unit='unit',\n",
+    "    time='period'\n",
+    ")\n",
+    "\n",
+    "print(results.summary())"
+   ]
   },
   {
    "cell_type": "code",
@@ -217,7 +243,33 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "# Effect of different nuclear norm regularization levels\nprint(\"Effect of nuclear norm regularization (λ_nn):\")\nprint(\"=\"*65)\nprint(f\"{'λ_nn':>10} {'ATT':>12} {'Bias':>12} {'Eff. Rank':>15}\")\nprint(\"-\"*65)\n\nfor lambda_nn in [0.0, 0.1, 1.0]:  # Reduced grid\n    trop_fixed = TROP(\n        lambda_time_grid=[1.0],  # Fixed\n        lambda_unit_grid=[1.0],  # Fixed\n        lambda_nn_grid=[lambda_nn],  # Vary this\n        n_bootstrap=20,  # Reduced for faster execution\n        seed=42\n    )\n    \n    res = trop_fixed.fit(\n        df,\n        outcome='outcome',\n        treatment='treated',\n        unit='unit',\n        time='period'\n    )\n    \n    bias = res.att - true_att\n    print(f\"{lambda_nn:>10.1f} {res.att:>12.4f} {bias:>12.4f} {res.effective_rank:>15.2f}\")"
+   "source": [
+    "# Effect of different nuclear norm regularization levels\n",
+    "print(\"Effect of nuclear norm regularization (λ_nn):\")\n",
+    "print(\"=\"*65)\n",
+    "print(f\"{'λ_nn':>10} {'ATT':>12} {'Bias':>12} {'Eff. Rank':>15}\")\n",
+    "print(\"-\"*65)\n",
+    "\n",
+    "for lambda_nn in [0.0, 0.1, 1.0]:  # Reduced grid\n",
+    "    trop_fixed = TROP(\n",
+    "        lambda_time_grid=[1.0],  # Fixed\n",
+    "        lambda_unit_grid=[1.0],  # Fixed\n",
+    "        lambda_nn_grid=[lambda_nn],  # Vary this\n",
+    "        n_bootstrap=20,  # Reduced for faster execution\n",
+    "        seed=42\n",
+    "    )\n",
+    "    \n",
+    "    res = trop_fixed.fit(\n",
+    "        df,\n",
+    "        outcome='outcome',\n",
+    "        treatment='treated',\n",
+    "        unit='unit',\n",
+    "        time='period'\n",
+    "    )\n",
+    "    \n",
+    "    bias = res.att - true_att\n",
+    "    print(f\"{lambda_nn:>10.1f} {res.att:>12.4f} {bias:>12.4f} {res.effective_rank:>15.2f}\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -353,7 +405,56 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "# SDID (no factor adjustment)\n# Note: SDID uses 'treat' (unit-level ever-treated indicator)\nsdid = SyntheticDiD(\n    n_bootstrap=50,  # Reduced for faster execution\n    seed=42\n)\n\n# SDID still uses post_periods parameter\nsdid_results = sdid.fit(\n    df,\n    outcome='outcome',\n    treatment='treat',  # Unit-level ever-treated indicator\n    unit='unit',\n    time='period',\n    post_periods=post_periods\n)\n\n# TROP (with factor adjustment)\n# Note: TROP uses 'treated' (observation-level treatment indicator)\n# and infers treatment periods automatically\ntrop_est2 = TROP(\n    lambda_nn_grid=[0.0, 0.1],  # Reduced grid for faster execution\n    n_bootstrap=50,  # Reduced for faster execution\n    seed=42\n)\n\ntrop_results = trop_est2.fit(\n    df,\n    outcome='outcome',\n    treatment='treated',  # Observation-level indicator\n    unit='unit',\n    time='period'\n)\n\nprint(\"Comparison: SDID vs TROP\")\nprint(\"=\"*60)\nprint(f\"True ATT: {true_att:.4f}\")\nprint()\nprint(f\"Synthetic DiD (no factor adjustment):\")\nprint(f\"  ATT: {sdid_results.att:.4f}\")\nprint(f\"  SE: {sdid_results.se:.4f}\")\nprint(f\"  Bias: {sdid_results.att - true_att:.4f}\")\nprint()\nprint(f\"TROP (with factor adjustment):\")\nprint(f\"  ATT: {trop_results.att:.4f}\")\nprint(f\"  SE: {trop_results.se:.4f}\")\nprint(f\"  Bias: {trop_results.att - true_att:.4f}\")\nprint(f\"  Effective rank: {trop_results.effective_rank:.2f}\")"
+   "source": [
+    "# SDID (no factor adjustment)\n",
+    "# Note: SDID uses 'treat' (unit-level ever-treated indicator)\n",
+    "sdid = SyntheticDiD(\n",
+    "    n_bootstrap=50,  # Reduced for faster execution\n",
+    "    seed=42\n",
+    ")\n",
+    "\n",
+    "# SDID still uses post_periods parameter\n",
+    "sdid_results = sdid.fit(\n",
+    "    df,\n",
+    "    outcome='outcome',\n",
+    "    treatment='treat',  # Unit-level ever-treated indicator\n",
+    "    unit='unit',\n",
+    "    time='period',\n",
+    "    post_periods=post_periods\n",
+    ")\n",
+    "\n",
+    "# TROP (with factor adjustment)\n",
+    "# Note: TROP uses 'treated' (observation-level treatment indicator)\n",
+    "# and infers treatment periods automatically\n",
+    "trop_est2 = TROP(\n",
+    "    lambda_nn_grid=[0.0, 0.1],  # Reduced grid for faster execution\n",
+    "    n_bootstrap=50,  # Reduced for faster execution\n",
+    "    seed=42\n",
+    ")\n",
+    "\n",
+    "trop_results = trop_est2.fit(\n",
+    "    df,\n",
+    "    outcome='outcome',\n",
+    "    treatment='treated',  # Observation-level indicator\n",
+    "    unit='unit',\n",
+    "    time='period'\n",
+    ")\n",
+    "\n",
+    "print(\"Comparison: SDID vs TROP\")\n",
+    "print(\"=\"*60)\n",
+    "print(f\"True ATT: {true_att:.4f}\")\n",
+    "print()\n",
+    "print(f\"Synthetic DiD (no factor adjustment):\")\n",
+    "print(f\"  ATT: {sdid_results.att:.4f}\")\n",
+    "print(f\"  SE: {sdid_results.se:.4f}\")\n",
+    "print(f\"  Bias: {sdid_results.att - true_att:.4f}\")\n",
+    "print()\n",
+    "print(f\"TROP (with factor adjustment):\")\n",
+    "print(f\"  ATT: {trop_results.att:.4f}\")\n",
+    "print(f\"  SE: {trop_results.se:.4f}\")\n",
+    "print(f\"  Bias: {trop_results.att - true_att:.4f}\")\n",
+    "print(f\"  Effective rank: {trop_results.effective_rank:.2f}\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -369,7 +470,83 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "# Monte Carlo comparison (reduced for faster tutorial execution)\nn_sims = 5  # Reduced from 20 for faster validation\ntrop_estimates = []\nsdid_estimates = []\n\nprint(f\"Running {n_sims} simulations...\")\n\nfor sim in range(n_sims):\n    # Generate new data using the library function\n    # (includes both 'treated' and 'treat' columns)\n    sim_data = generate_factor_data(\n        n_units=50,\n        n_pre=10,\n        n_post=5,\n        n_treated=10,\n        n_factors=2,\n        treatment_effect=2.0,\n        factor_strength=1.5,\n        noise_sd=0.5,\n        seed=100 + sim\n    )\n    \n    # TROP (uses observation-level 'treated')\n    # Note: TROP infers treatment periods from the treatment indicator\n    try:\n        trop_m = TROP(\n            lambda_time_grid=[1.0],\n            lambda_unit_grid=[1.0],\n            lambda_nn_grid=[0.1],\n            n_bootstrap=10, \n            seed=42 + sim\n        )\n        trop_res = trop_m.fit(\n            sim_data,\n            outcome='outcome',\n            treatment='treated',\n            unit='unit',\n            time='period'\n        )\n        trop_estimates.append(trop_res.att)\n    except Exception as e:\n        print(f\"TROP failed on sim {sim}: {e}\")\n    \n    # SDID (uses unit-level 'treat')\n    # Note: SDID still uses post_periods parameter\n    try:\n        sdid_m = SyntheticDiD(n_bootstrap=10, seed=42 + sim)\n        sdid_res = sdid_m.fit(\n            sim_data,\n            outcome='outcome',\n            treatment='treat',  # Unit-level ever-treated indicator\n            unit='unit',\n            time='period',\n            post_periods=list(range(10, 15))\n        )\n        sdid_estimates.append(sdid_res.att)\n    except Exception as e:\n        print(f\"SDID failed on sim {sim}: {e}\")\n\nprint(f\"\\nMonte Carlo Results (True ATT = {true_att})\")\nprint(\"=\"*60)\nprint(f\"{'Estimator':<15} {'Mean':>12} {'Bias':>12} {'RMSE':>12}\")\nprint(\"-\"*60)\n\nif trop_estimates:\n    trop_mean = np.mean(trop_estimates)\n    trop_bias = trop_mean - true_att\n    trop_rmse = np.sqrt(np.mean([(e - true_att)**2 for e in trop_estimates]))\n    print(f\"{'TROP':<15} {trop_mean:>12.4f} {trop_bias:>12.4f} {trop_rmse:>12.4f}\")\n\nif sdid_estimates:\n    sdid_mean = np.mean(sdid_estimates)\n    sdid_bias = sdid_mean - true_att\n    sdid_rmse = np.sqrt(np.mean([(e - true_att)**2 for e in sdid_estimates]))\n    print(f\"{'SDID':<15} {sdid_mean:>12.4f} {sdid_bias:>12.4f} {sdid_rmse:>12.4f}\")"
+   "source": [
+    "# Monte Carlo comparison (reduced for faster tutorial execution)\n",
+    "n_sims = 5  # Reduced from 20 for faster validation\n",
+    "trop_estimates = []\n",
+    "sdid_estimates = []\n",
+    "\n",
+    "print(f\"Running {n_sims} simulations...\")\n",
+    "\n",
+    "for sim in range(n_sims):\n",
+    "    # Generate new data using the library function\n",
+    "    # (includes both 'treated' and 'treat' columns)\n",
+    "    sim_data = generate_factor_data(\n",
+    "        n_units=50,\n",
+    "        n_pre=10,\n",
+    "        n_post=5,\n",
+    "        n_treated=10,\n",
+    "        n_factors=2,\n",
+    "        treatment_effect=2.0,\n",
+    "        factor_strength=1.5,\n",
+    "        noise_sd=0.5,\n",
+    "        seed=100 + sim\n",
+    "    )\n",
+    "    \n",
+    "    # TROP (uses observation-level 'treated')\n",
+    "    # Note: TROP infers treatment periods from the treatment indicator\n",
+    "    try:\n",
+    "        trop_m = TROP(\n",
+    "            lambda_time_grid=[1.0],\n",
+    "            lambda_unit_grid=[1.0],\n",
+    "            lambda_nn_grid=[0.1],\n",
+    "            n_bootstrap=10, \n",
+    "            seed=42 + sim\n",
+    "        )\n",
+    "        trop_res = trop_m.fit(\n",
+    "            sim_data,\n",
+    "            outcome='outcome',\n",
+    "            treatment='treated',\n",
+    "            unit='unit',\n",
+    "            time='period'\n",
+    "        )\n",
+    "        trop_estimates.append(trop_res.att)\n",
+    "    except Exception as e:\n",
+    "        print(f\"TROP failed on sim {sim}: {e}\")\n",
+    "    \n",
+    "    # SDID (uses unit-level 'treat')\n",
+    "    # Note: SDID still uses post_periods parameter\n",
+    "    try:\n",
+    "        sdid_m = SyntheticDiD(n_bootstrap=10, seed=42 + sim)\n",
+    "        sdid_res = sdid_m.fit(\n",
+    "            sim_data,\n",
+    "            outcome='outcome',\n",
+    "            treatment='treat',  # Unit-level ever-treated indicator\n",
+    "            unit='unit',\n",
+    "            time='period',\n",
+    "            post_periods=list(range(10, 15))\n",
+    "        )\n",
+    "        sdid_estimates.append(sdid_res.att)\n",
+    "    except Exception as e:\n",
+    "        print(f\"SDID failed on sim {sim}: {e}\")\n",
+    "\n",
+    "print(f\"\\nMonte Carlo Results (True ATT = {true_att})\")\n",
+    "print(\"=\"*60)\n",
+    "print(f\"{'Estimator':<15} {'Mean':>12} {'Bias':>12} {'RMSE':>12}\")\n",
+    "print(\"-\"*60)\n",
+    "\n",
+    "if trop_estimates:\n",
+    "    trop_mean = np.mean(trop_estimates)\n",
+    "    trop_bias = trop_mean - true_att\n",
+    "    trop_rmse = np.sqrt(np.mean([(e - true_att)**2 for e in trop_estimates]))\n",
+    "    print(f\"{'TROP':<15} {trop_mean:>12.4f} {trop_bias:>12.4f} {trop_rmse:>12.4f}\")\n",
+    "\n",
+    "if sdid_estimates:\n",
+    "    sdid_mean = np.mean(sdid_estimates)\n",
+    "    sdid_bias = sdid_mean - true_att\n",
+    "    sdid_rmse = np.sqrt(np.mean([(e - true_att)**2 for e in sdid_estimates]))\n",
+    "    print(f\"{'SDID':<15} {sdid_mean:>12.4f} {sdid_bias:>12.4f} {sdid_rmse:>12.4f}\")"
+   ]
   },
   {
    "cell_type": "code",
@@ -407,7 +584,27 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "# One-liner estimation with default tuning grid\n# Note: TROP infers treatment periods from the treatment indicator\nquick_results = trop(\n    df,\n    outcome='outcome',\n    treatment='treated',\n    unit='unit',\n    time='period',\n    n_bootstrap=20,  # Reduced for faster execution\n    seed=42\n)\n\nprint(f\"Quick estimation:\")\nprint(f\"  ATT: {quick_results.att:.4f}\")\nprint(f\"  SE: {quick_results.se:.4f}\")\nprint(f\"  λ_time: {quick_results.lambda_time:.2f}\")\nprint(f\"  λ_unit: {quick_results.lambda_unit:.2f}\")\nprint(f\"  λ_nn: {quick_results.lambda_nn:.2f}\")\nprint(f\"  Effective rank: {quick_results.effective_rank:.2f}\")"
+   "source": [
+    "# One-liner estimation with default tuning grid\n",
+    "# Note: TROP infers treatment periods from the treatment indicator\n",
+    "quick_results = trop(\n",
+    "    df,\n",
+    "    outcome='outcome',\n",
+    "    treatment='treated',\n",
+    "    unit='unit',\n",
+    "    time='period',\n",
+    "    n_bootstrap=20,  # Reduced for faster execution\n",
+    "    seed=42\n",
+    ")\n",
+    "\n",
+    "print(f\"Quick estimation:\")\n",
+    "print(f\"  ATT: {quick_results.att:.4f}\")\n",
+    "print(f\"  SE: {quick_results.se:.4f}\")\n",
+    "print(f\"  λ_time: {quick_results.lambda_time:.2f}\")\n",
+    "print(f\"  λ_unit: {quick_results.lambda_unit:.2f}\")\n",
+    "print(f\"  λ_nn: {quick_results.lambda_nn:.2f}\")\n",
+    "print(f\"  Effective rank: {quick_results.effective_rank:.2f}\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -425,7 +622,34 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "# Compare variance estimation methods\nprint(\"Variance estimation comparison:\")\nprint(\"=\"*50)\n\nfor method in ['bootstrap', 'jackknife']:\n    trop_var = TROP(\n        lambda_time_grid=[1.0],\n        lambda_unit_grid=[1.0], \n        lambda_nn_grid=[0.1],\n        variance_method=method,\n        n_bootstrap=30,  # Reduced for faster execution\n        seed=42\n    )\n    \n    res = trop_var.fit(\n        df,\n        outcome='outcome',\n        treatment='treated',\n        unit='unit',\n        time='period'\n    )\n    \n    print(f\"\\n{method.capitalize()}:\")\n    print(f\"  ATT: {res.att:.4f}\")\n    print(f\"  SE: {res.se:.4f}\")\n    print(f\"  95% CI: [{res.conf_int[0]:.4f}, {res.conf_int[1]:.4f}]\")"
+   "source": [
+    "# Compare variance estimation methods\n",
+    "print(\"Variance estimation comparison:\")\n",
+    "print(\"=\"*50)\n",
+    "\n",
+    "for method in ['bootstrap', 'jackknife']:\n",
+    "    trop_var = TROP(\n",
+    "        lambda_time_grid=[1.0],\n",
+    "        lambda_unit_grid=[1.0], \n",
+    "        lambda_nn_grid=[0.1],\n",
+    "        variance_method=method,\n",
+    "        n_bootstrap=30,  # Reduced for faster execution\n",
+    "        seed=42\n",
+    "    )\n",
+    "    \n",
+    "    res = trop_var.fit(\n",
+    "        df,\n",
+    "        outcome='outcome',\n",
+    "        treatment='treated',\n",
+    "        unit='unit',\n",
+    "        time='period'\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"\\n{method.capitalize()}:\")\n",
+    "    print(f\"  ATT: {res.att:.4f}\")\n",
+    "    print(f\"  SE: {res.se:.4f}\")\n",
+    "    print(f\"  95% CI: [{res.conf_int[0]:.4f}, {res.conf_int[1]:.4f}]\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -508,4 +732,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "maturin"
 
 [project]
 name = "diff-diff"
-version = "2.1.7"
+version = "2.1.8"
 description = "A library for Difference-in-Differences causal inference analysis"
 readme = "README.md"
 license = "MIT"

rust/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "diff_diff_rust"
-version = "2.1.7"
+version = "2.1.8"
 edition = "2021"
 description = "Rust backend for diff-diff DiD library"
 license = "MIT"