Sparse Synthetic Control (SparseSC) =================================== .. currentmodule:: mlsynth Overview -------- SparseSC implements the L1-penalized predictor-weighting variant of canonical synthetic control proposed by Vives-i-Bastida (2023, *Predictor Selection for Synthetic Controls*). It targets the same Abadie, Diamond, and Hainmueller (2010) framework as classical SCM, but adds a *lasso* penalty on the predictor-importance vector :math:`\mathbf{v}` to deliver interpretable *predictor selection*: as the L1 penalty grows, uninformative predictors get :math:`v_p`-weights of exactly zero and are dropped from the fit. Compared with the canonical SCM data-driven :math:`\mathbf{V}` choice (a cross-validated grid search over diagonal :math:`\mathbf{V}` minimizing pre-period MSE), SparseSC * selects predictors explicitly via L1 sparsity rather than implicitly via small but nonzero :math:`v_p`-weights; * picks the L1 penalty :math:`\lambda` on a held-out validation block of the pre-period (a 75/25 train/validation split by default, which matches the 14/5-year split Vives used in the empirical Prop 99 application); and * anchors the first predictor's :math:`v_p`-weight at 1, which fixes the overall scale and removes the trivial :math:`\mathbf{v} = \mathbf{0}` minimum that the L1 penalty would otherwise admit. The donor weights :math:`\mathbf{w}` solve the usual SCM simplex QP given :math:`\mathbf{v}`. Inference defaults to a moving-block conformal CI for the ATT in the spirit of Chernozhukov, Wuethrich and Zhu (2021), calibrated on the validation-block residuals. Vives's Abadie-style placebo permutation is still available via ``inference_method="placebo"``. When to use this estimator -------------------------- Reach for SparseSC when you have one treated unit, a rich predictor set, and you want the fit to tell you *which* predictors matter rather than carry all of them with small but nonzero weights. The lasso penalty on the predictor-importance vector drives the uninformative predictors to exactly zero, so the synthetic control's explanation of the treated pre-trajectory is interpretable in terms of a small, named subset. A concrete example: a state passes a tobacco-control law and you have dozens of candidate predictors of cigarette sales -- prices, income, beer consumption, the youth share, and several lagged outcomes. With the canonical data-driven :math:`\mathbf{V}` every predictor keeps some weight and the story is muddy. SparseSC prunes the over-rich set down to the handful that actually drive the pre-law fit, selects the L1 penalty on a held-out validation block, and reads the policy effect as the post-law gap -- with a conformal interval calibrated on the validation residuals rather than a coarse donor-permutation grid. Notation -------- Let :math:`j = 1` denote the treated unit, with all units :math:`\mathcal{N} \coloneqq \{1, \dots, N\}` and donor pool :math:`\mathcal{N}_0 \coloneqq \mathcal{N} \setminus \{1\}` of cardinality :math:`N_0`. Time runs over :math:`t \in \mathcal{T} \coloneqq \{1, \dots, T\}`, 1-indexed; the intervention takes effect after period :math:`T_0`, splitting :math:`\mathcal{T}` into the pre-period :math:`\mathcal{T}_1 \coloneqq \{t \in \mathcal{T} : t \le T_0\}` (of length :math:`T_0`) and the post-period :math:`\mathcal{T}_2 \coloneqq \{t \in \mathcal{T} : t > T_0\}`. The treated outcome is :math:`y_{1t}`; each donor :math:`j \in \mathcal{N}_0` contributes a series :math:`\mathbf{y}_j`, stacked into the donor matrix :math:`\mathbf{Y}_0 \coloneqq [\mathbf{y}_j]_{j \in \mathcal{N}_0} \in \mathbb{R}^{T \times N_0}` (one column per donor); write :math:`\mathbf{y}_{0t} \in \mathbb{R}^{N_0}` for the donor outcomes at time :math:`t` (the :math:`t`-th row). Donor weights are :math:`\mathbf{w} \in \mathbb{R}^{N_0}`, constrained to the unit simplex :math:`\Delta^{N_0} \coloneqq \{\mathbf{w} \in \mathbb{R}_{\ge 0}^{N_0} : \|\mathbf{w}\|_1 = 1\}`; the optimiser is :math:`\mathbf{w}^\ast`. The synthetic counterfactual is :math:`\widehat{y}_{1t} \coloneqq \mathbf{y}_{0t}^\top \mathbf{w}^\ast`, the per-period effect is :math:`\tau_t \coloneqq y_{1t} - \widehat{y}_{1t}`, and the ATT is :math:`\widehat{\tau} \coloneqq |\mathcal{T}_2|^{-1} \sum_{t \in \mathcal{T}_2} \tau_t`. The significance level is :math:`\alpha`. Predictors enter through a treated vector :math:`\mathbf{x}_1 \in \mathbb{R}^P` and a donor matrix :math:`\mathbf{X}_0 \in \mathbb{R}^{P \times N_0}` over :math:`P` predictors; after standardization each row of :math:`[\mathbf{X}_0, \mathbf{x}_1]` has unit sample standard deviation across units. The predictor-importance vector is :math:`\mathbf{v} \in \mathbb{R}^P_{\ge 0}` with components :math:`v_p`; the L1 penalty strength is :math:`\lambda \ge 0`, and :math:`\mathbf{V} = \mathrm{diag}(\mathbf{v})` is the diagonal predictor-weight matrix. The pre-period is further split for tuning. The training block is :math:`\mathcal{T}_1^{\mathrm{tr}} \coloneqq \{1, \dots, T_0^{\mathrm{tr}}\}` and the validation block is :math:`\mathcal{T}_1^{\mathrm{val}} \coloneqq \{T_0^{\mathrm{tr}} + 1, \dots, T_0\}`, with :math:`\mathcal{T}_1 = \mathcal{T}_1^{\mathrm{tr}} \cup \mathcal{T}_1^{\mathrm{val}}` (default 75/25). This pre-period split is internal to predictor and penalty selection and is distinct from the canonical pre/post split at :math:`T_0`. Identifying assumptions ----------------------- 1. Pre-treatment fit / convex-hull support. There exist weights :math:`\mathbf{w} \in \Delta^{N_0}` and predictor weights :math:`\mathbf{v}` under which the treated pre-period predictors are matched by the donors, :math:`\mathbf{x}_1 \approx \mathbf{X}_0 \mathbf{w}` -- equivalently, the treated unit lies inside (or near) the convex hull of the donors over the selected predictors (Abadie, Diamond & Hainmueller 2010). *Remark.* This is the workhorse identifying condition for any synthetic control: a good pre-period match is the empirical certificate one inspects. SparseSC adds that the match should be achievable with *few* predictors -- if it is, the lasso recovers a sparse, interpretable explanation; if the treated unit can only be matched by leaning on many predictors at once, the selected set will not be sparse. 2. Informative anchor. The first predictor (the anchor, with :math:`v_1 = 1`) is genuinely informative about the treated unit's pre-trajectory (Vives 2023, Appendix 6.1). *Remark.* The anchor fixes the overall scale of :math:`\mathbf{v}` and removes the trivial :math:`\mathbf{v} = \mathbf{0}` minimum the L1 penalty would otherwise admit. Because the anchor's weight is pinned at 1, a poorly chosen anchor biases the fit; Vives recommends picking a predictor known to be informative, or treating the anchor as a hyperparameter and sweeping it. 3. No anticipation. Treatment has no effect before :math:`T_0`: :math:`y_{1t} = y_{1t}^N` for all :math:`t \in \mathcal{T}_1`, so the pre-period outcomes and predictors reflect the no-intervention path. *Remark.* If the treated unit reacts in advance of the formal intervention date, the pre-period fit -- and the validation-block residuals the conformal interval is calibrated on -- are contaminated by the effect itself. Date :math:`T_0` at the first plausible response, not the nominal policy date. 4. Outcome-model stability. The no-intervention outcomes follow a stable data-generating process across :math:`\mathcal{T}`, so weights selected on :math:`\mathcal{T}_1` continue to reproduce the treated unit's no-intervention path on :math:`\mathcal{T}_2` (Abadie, Diamond & Hainmueller 2010). *Remark.* This is what licenses extrapolating the pre-period fit forward, and it is also what the validation block stress-tests: holding out :math:`\mathcal{T}_1^{\mathrm{val}}` checks that the selected predictors and penalty generalise within the pre-period before they are asked to generalise past :math:`T_0`. Mathematical Formulation ------------------------ Inner W-weight QP ^^^^^^^^^^^^^^^^^ Given :math:`\mathbf{v} \in \mathbb{R}^P_{\ge 0}` the donor weights solve .. math:: \mathbf{w}^\ast(\mathbf{v}) = \operatorname*{argmin}_{\mathbf{w} \in \Delta^{N_0}} \; \mathbf{w}^\top \mathbf{X}_0^\top \mathrm{diag}(\mathbf{v})\, \mathbf{X}_0\, \mathbf{w} - 2\, \mathbf{x}_1^\top \mathrm{diag}(\mathbf{v})\, \mathbf{X}_0\, \mathbf{w}, where :math:`\Delta^{N_0} = \{\mathbf{w} \in \mathbb{R}^{N_0}_{\ge 0} : \mathbf{1}^\top \mathbf{w} = 1\}` is the donor simplex. This is exactly the QP MATLAB's ``quadprog`` solves inside ``sparse_synth/loss_function.m``. :mod:`mlsynth` calls Clarabel directly (bypassing CVXPY's canonicalization layer), which is the single biggest performance fix versus the prior CVXPY-based implementation: CVXPY parsing overhead was ~10-50 ms per call for a 39-donor problem, while the underlying Clarabel solve itself takes microseconds. The constraint skeleton ``(A, b, cones, settings)`` is cached per donor count :math:`N_0` so only the data terms :math:`\mathbf{H} = \mathbf{X}_0^\top \mathrm{diag}(\mathbf{v}) \mathbf{X}_0` and :math:`\mathbf{q} = -2 \mathbf{X}_0^\top \mathrm{diag}(\mathbf{v}) \mathbf{x}_1` are rebuilt per call. For numerical robustness — the augmented k > N spec is rank- deficient and Clarabel can return ``InsufficientProgress`` at tight tolerance — the inner solve retries with a trace-scaled ridge and looser tolerances before falling back to a uniform-w feasible point. This prevents the outer L-BFGS-B sweep from aborting mid-run on a single bad exploration step. Outer V-weight problem ^^^^^^^^^^^^^^^^^^^^^^ The :math:`\mathbf{v}`-weights minimize a penalized outcome MSE plus the L1 penalty on :math:`\mathbf{v}`, over a window :math:`\mathcal{W}`: .. math:: \mathbf{v}^\ast(\lambda) = \operatorname*{argmin}_{\mathbf{v} \in \mathbb{R}^P_{\ge 0},\; v_1 = 1} \; \frac{1}{|\mathcal{W}|} \sum_{t \in \mathcal{W}} \bigl(y_{1t} - \mathbf{y}_{0t}^\top \mathbf{w}^\ast(\mathbf{v})\bigr)^2 + \lambda \, \|\mathbf{v}\|_1 . The :math:`v_1 = 1` anchor is what prevents the trivial all-zero solution at any :math:`\lambda > 0`: without it the outer objective is positive-scale-invariant in :math:`\mathbf{v}` and the L1 penalty would push every component to zero (Vives 2023, Appendix 6.1). The window :math:`\mathcal{W}` is set by ``outer_loss_window``: * ``"training"`` (default) — :math:`\mathcal{W} = \mathcal{T}_1^{\mathrm{tr}}`. Matches the unpublished MATLAB driver ``sparse_synth.m`` and reproduces the Prop 99 estimates Vives reports in the empirical section. * ``"validation"`` — :math:`\mathcal{W} = \mathcal{T}_1^{\mathrm{val}}`. Matches the page-5 :math:`L_V` definition in Vives's Algorithm 1 literally; useful for ablations but produces notably worse in-sample fit than the training variant. Each evaluation of the outer objective invokes the inner QP, so the outer problem is a smooth bound-constrained NLP solved with L-BFGS-B (``scipy.optimize``). Gradient computation ~~~~~~~~~~~~~~~~~~~~ L-BFGS-B needs gradients of the outer objective in :math:`\mathbf{v}`. Two modes are available, controlled by ``use_analytical_grad``: * ``False`` (default) — central-difference numerical gradient. Each outer step pays :math:`2(P-1)` inner-QP solves. * ``True`` — closed-form gradient via the envelope theorem applied at the inner optimum :math:`\mathbf{w}^\ast(\mathbf{v})`. With active set :math:`\mathcal{A} = \{i : w_i^\ast > 0\}`, one :math:`(|\mathcal{A}| + 1) \times (|\mathcal{A}| + 1)` Cholesky on the reduced KKT matrix yields all :math:`P - 1` gradient components in :math:`O(P |\mathcal{A}|)` work: .. math:: \frac{\partial \mathcal{L}}{\partial v_p} = -\frac{4}{|\mathcal{W}|}\, r_p \cdot \bigl(\mathbf{X}_0[p, \mathcal{A}]\, \mathbf{z}\bigr) + \lambda, where :math:`r_p = x_{1p} - \mathbf{X}_0[p, \mathcal{A}]\, \mathbf{w}_{\mathcal{A}}^\ast` is the predictor-:math:`p` pre-fit residual and :math:`\mathbf{z}` solves .. math:: \begin{pmatrix} 2 \mathbf{H}_{\mathcal{A}\mathcal{A}} & \mathbf{1} \\ \mathbf{1}^\top & 0 \end{pmatrix} \begin{pmatrix} \mathbf{z} \\ \mu_z \end{pmatrix} = \begin{pmatrix} \mathbf{Z}_0[:, \mathcal{A}]^\top \mathbf{r}_{\text{outer}} \\ 0 \end{pmatrix}. The analytical gradient is exact (verified against central FD to ~1e-7 at random interior points). It yields a ~5–10× speedup on the outer sweep, but the cleaner gradient lets L-BFGS-B settle at the first critical point near the cold init on the non-convex L1- penalized V-objective. The FD path's implicit gradient noise tends to find better local optima at non-zero lambda, so the default is FD for correctness. Opt in to the analytical path when running large placebo sweeps where throughput matters more than exact local-optimum reproducibility. When ``use_analytical_grad = True``, the L-BFGS-B ``ftol`` auto-tightens to ``1e-12`` because the clean gradient converges in many fewer iterations and the default ``1e-8`` terminates the loop before convergence. Lambda selection ^^^^^^^^^^^^^^^^ The penalty :math:`\lambda` is selected by the *unpenalized* validation-block outcome MSE: .. math:: \widehat{\lambda} = \operatorname*{argmin}_{\lambda \in \Lambda} \; \frac{1}{T_0 - T_0^{\mathrm{tr}}} \sum_{t \in \mathcal{T}_1^{\mathrm{val}}} \bigl(y_{1t} - \mathbf{y}_{0t}^\top \mathbf{w}^\ast(\mathbf{v}^\ast(\lambda))\bigr)^2 . The default grid is :math:`\Lambda = \{0\} \cup \text{logspace}(10^{-4}, 1, 50)`. Setting :math:`\lambda = 0` recovers the unpenalized data-driven SCM with a unit-anchored first predictor. Predictor selection ^^^^^^^^^^^^^^^^^^^ As :math:`\lambda` grows, the L1 penalty drives uninformative :math:`v_p` to exactly zero; the corresponding predictor effectively drops out of the fit. The selected predictor set is .. math:: \mathcal{S}(\widehat{\lambda}) = \{p : v_p^\ast(\widehat{\lambda}) > 0\}. This is what makes the method *Sparse* SC: the explanation of the treated unit's pre-trajectory is interpretable in terms of a small subset of predictors. ATT and Counterfactual ^^^^^^^^^^^^^^^^^^^^^^ With :math:`\widehat{\mathbf{v}} = \mathbf{v}^\ast(\widehat{\lambda})` and :math:`\widehat{\mathbf{w}} = \mathbf{w}^\ast(\widehat{\mathbf{v}})` recovered on the full pre-period, the counterfactual and ATT are .. math:: \widehat{y}_{1t} = \mathbf{y}_{0t}^\top \widehat{\mathbf{w}}, \qquad \widehat{\tau} = \frac{1}{T - T_0} \sum_{t = T_0 + 1}^T \bigl(y_{1t} - \widehat{y}_{1t}\bigr). Conformal ATT inference (default) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Inference defaults to a moving-block conformal CI for the ATT, following the philosophy of Chernozhukov, Wuethrich and Zhu (2021): treat the in-sample residuals as a *calibration sample* of what "noise" should look like under the no-treatment null, and invert a permutation test in :math:`\theta` to bracket the ATT. Define the residual series :math:`e_t = y_{1t} - \mathbf{y}_{0t}^\top \widehat{\mathbf{w}}`. The calibration set is .. math:: e^{\text{calib}} = \begin{cases} \{e_t : t \in (T_0^{\text{tr}}, T_0]\} & \text{if ``conformal\_window = "validation"`` (default)} \\ \{e_t : t \in [1, T_0]\} & \text{if ``conformal\_window = "pre"``} \end{cases}. The validation block is genuinely out-of-sample under the chosen :math:`\mathbf{v}`; the full pre-block gives a larger calibration sample but its training-block residuals are in-sample under :math:`\mathbf{v}`. The conformity score for a block :math:`B` of size :math:`b = \max(3, \lfloor\sqrt{T - T_0}\rfloor)` is .. math:: s(B) = \frac{1}{b}\sum_{t \in B} |e_t|, and the calibration distribution is built by sliding the block across :math:`e^{\text{calib}}` (with wrap-around blocks for boundary coverage). The post-treatment test statistic at the candidate ATT :math:`\theta` is .. math:: s_{\text{post}}(\theta) = \frac{1}{T - T_0}\sum_{t = T_0 + 1}^T \bigl|e_t - \theta\bigr|. The :math:`(1 - \alpha)` conformal CI is .. math:: \mathrm{CI}_{1 - \alpha} = \{\theta : \Pr_{B}\bigl(s(B) \ge s_{\text{post}}(\theta)\bigr) > \alpha\}, which we compute by grid search over a generous neighbourhood of :math:`\widehat{\mathrm{ATT}}`. The two-sided p-value for :math:`H_0 : \mathrm{ATT} = 0` is .. math:: p_{\text{conf}} = \Pr_{B}\bigl(s(B) \ge s_{\text{post}}(0)\bigr) = \frac{1}{|\mathcal{B}|}\sum_{B \in \mathcal{B}} \mathbb{1}\{s(B) \ge s_{\text{post}}(0)\}. Pointwise per-period bands use the :math:`(1 - \alpha)`-quantile of the calibration scores directly: .. math:: [e_t - q_{1 - \alpha},\; e_t + q_{1 - \alpha}], \quad q_{1 - \alpha} = \mathrm{Quantile}_{1 - \alpha}\{s(B)\}. This inferential procedure trades the cross-donor exchangeability assumption of Vives's placebo (every donor is equally likely to be the treated unit) for a within-unit exchangeability assumption on the residuals (validation-period residuals look like the no-treatment counterfactual's noise). On Prop 99 the conformal 95% CI is typically :math:`[-20, -18]` versus the placebo's much wider bounds, because conformal leverages the actual model's residual structure rather than donor-level heterogeneity. Abadie-style placebo (opt-in) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Set ``inference_method="placebo"`` to recover Vives's procedure. For each donor :math:`j`, swap that donor into the treated slot, remove it from the donor pool, refit SparseSC *at the already- selected* :math:`\widehat\lambda` (or, optionally, re-run the full :math:`\lambda` sweep) and record the placebo ATT. The two-sided permutation p-value is .. math:: p = \frac{\#\{j : |\mathrm{ATT}_j^{\text{placebo}}| \ge |\widehat{\mathrm{ATT}}|\} + 1}{B + 1}, where :math:`B` is the number of completed placebos. Re-using :math:`\widehat\lambda` makes the placebo loop tractable; set ``placebo_resweep=True`` to re-select :math:`\lambda` for every placebo (much slower). Predictor Convention -------------------- Like every other :mod:`mlsynth` estimator, SparseSC is fed a single long-format ``df`` with one row per ``(unit, time)``. Predictors are constructed under the hood from the same frame, in two flavors: * ``covariates`` — column names in ``df`` whose per-unit pre- treatment mean is taken as the predictor value. Time-invariant unit characteristics collapse trivially; time-varying covariates are summarized by the pre-period mean. * ``outcome_lag_periods`` — specific pre-treatment time labels (as found in the ``time`` column) whose outcome values become additional predictor rows. These are the canonical Abadie, Diamond & Hainmueller (2010) lagged-outcome predictors (e.g., the ``smk_75``, ``smk_80``, ``smk_88`` rows in the Prop 99 example). The two lists are concatenated to form the predictor matrix; the first predictor (first entry of ``covariates`` if any, otherwise the first outcome lag) is the *anchor* whose :math:`v`-weight is fixed at 1. The anchor choice matters in finite samples — Vives recommends picking a predictor known to be informative, or treating the anchor as a hyperparameter and sweeping it (Vives 2023, Appendix 6.1). Performance notes ----------------- The single biggest cost in a SparseSC fit is the inner W-weight QP, which is invoked .. math:: |\Lambda| \times (\text{outer iters}) \times g times where :math:`g = 2(P-1)` under finite-difference gradients and :math:`g = 1` under the analytical gradient. For Vives's augmented k=40 spec that's ~50,000 inner-QP calls just for the fit, plus another :math:`B \approx 38` placebos under ``inference_method="placebo"``. Two optimizations in this build matter: * Direct Clarabel removes CVXPY canonicalization (~30-60× per call). Speedup applies universally; no correctness tradeoff. * Analytical gradient (opt-in via ``use_analytical_grad=True``) removes the :math:`2(P-1)` finite-difference factor (~5-10× on the outer loop). Tradeoff: the cleaner gradient can settle in worse local optima of the non-convex L1-penalized outer objective; FD's implicit gradient noise tends to escape them. Default off for correctness. Empirically, the combination puts the canonical ADH-7 California Prop 99 fit at ~5 s with analytical gradient and ~23 s with FD (versus a CVXPY+SCS baseline that would hang for minutes on the augmented k=40 spec). Core API -------- .. automodule:: mlsynth.estimators.sparse_sc :members: :undoc-members: :show-inheritance: Configuration ------------- .. autoclass:: mlsynth.config_models.SparseSCConfig :members: :undoc-members: Helper Modules -------------- .. automodule:: mlsynth.utils.sparse_sc_helpers.setup :members: :undoc-members: .. automodule:: mlsynth.utils.sparse_sc_helpers.inner :members: :undoc-members: .. automodule:: mlsynth.utils.sparse_sc_helpers.objective :members: :undoc-members: .. automodule:: mlsynth.utils.sparse_sc_helpers.optimization :members: :undoc-members: .. automodule:: mlsynth.utils.sparse_sc_helpers.inference :members: :undoc-members: .. automodule:: mlsynth.utils.sparse_sc_helpers.plotter :members: :undoc-members: .. automodule:: mlsynth.utils.sparse_sc_helpers.structures :members: :undoc-members: Replication ----------- SparseSC is verified on the canonical Proposition 99 panel: handed an over-rich augmented predictor set, the L1 penalty prunes to 6 of 33 predictors and the effect lands at :math:`-17.9` packs (95% conformal CI :math:`[-21.3, -15.4]`) on the Abadie-Diamond-Hainmueller donor pool. See the dedicated :doc:`replication page ` for the full specification, code, and number-match. Example ------- The canonical empirical example is Vives's augmented California Proposition 99 study. Load the reshaped long-form panel and run SparseSC with the original ADH-7 predictor set: .. code-block:: python """Run SparseSC on the long-form augmented California dataset.""" from __future__ import annotations import pandas as pd from mlsynth import SparseSC # --------------------------------------------------------------------- # Load long-form panel # --------------------------------------------------------------------- df = pd.read_csv( "https://raw.githubusercontent.com/jgreathouse9/mlsynth/refs/heads/main/basedata/augmented_cali_long.csv" ) LAG_PERIODS = [1975, 1980, 1988] COVARIATES = [ "p_cig", "loginc", "pct15-24", "pc_beer", ] # --------------------------------------------------------------------- # SparseSC fit # --------------------------------------------------------------------- results = SparseSC( { "df": df, "outcome": "cigsale", "treat": "Proposition 99", "unitid": "state", "time": "year", "covariates": COVARIATES, "outcome_lag_periods": LAG_PERIODS, "display_graphs": True, "run_inference": False, } ).fit() Enable inference (validation-block conformal is the default) and inspect the ATT CI: .. code-block:: python results = SparseSC({ "df": df, "outcome": "cigsale", "treat": "Proposition 99", "unitid": "state", "time": "year", "covariates": COVARIATES, "outcome_lag_periods": LAG_PERIODS, "alpha": 0.05, # CI level "run_inference": True, "display_graphs": False, }).fit() print(results.att) # post-period ATT lo, hi = results.att_ci # 95% conformal CI (standardized) print(lo, hi) print(results.inference.p_value) # H_0: ATT = 0 (standardized slot) print(results.inference_detail.method) # raw placebo/conformal object print(results.design.opt_lambda) # selected L1 penalty print(results.predictor_weights) # {predictor: v_p} print(results.donor_weights) # {donor: w_j} on the simplex # Lambda sweep diagnostics. import matplotlib.pyplot as plt plt.plot(results.design.lambda_grid, results.design.val_mse_curve) plt.xscale("log"); plt.xlabel("lambda"); plt.ylabel("validation MSE") References ---------- Abadie, A., Diamond, A., & Hainmueller, J. (2010). "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program." *Journal of the American Statistical Association* 105(490):493-505. Chernozhukov, V., Wuethrich, K., & Zhu, Y. (2021). "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls." *Journal of the American Statistical Association* 116(536):1849-1864. Vives-i-Bastida, J. (2023). "Predictor Selection for Synthetic Controls." arXiv:2203.11576v2.