pyrollmatch 0.0.7

Fast rolling entry matching for staggered adoption studies using polars + numpy

pyrollmatch

Alpha (v0.1.x) — This package is in early development. Results have been validated against R rollmatch on synthetic data, but edge cases may remain. APIs are not stable and may change without notice. Please verify critical results independently.

Fast rolling entry matching for staggered adoption studies in Python.

High-performance reimplementation of the R rollmatch package using polars + numpy, with 8 propensity score models and comprehensive post-matching diagnostics.

	R rollmatch	pyrollmatch
Max scale	~5K treated (crashes)	90K+ treated
10K × 30K	OOM (5.8B row join)	1.7 seconds
Scoring models	Logistic only	8 models
Diagnostics	SMD only	SMD + t-test + VR + KS + TOST
Data library	dplyr (deprecated APIs)	polars

Installation

pip install pyrollmatch

From source:

git clone https://github.com/AlanHuang99/pyrollmatch.git
cd pyrollmatch
pip install -e ".[dev]"

Quick Start

import polars as pl
from pyrollmatch import rollmatch, alpha_sweep

# Panel data: unit × time with treatment indicator
data = pl.read_parquet("panel_data.parquet")

# Rolling entry matching
result = rollmatch(
    data,
    treat="treat",              # binary treatment group indicator (1=treated, 0=control)
    tm="time_period",           # time period (integer)
    entry="entry_time",         # treatment onset period for treated units;
                                # for controls, set to any value > max(time_period)
    id="unit_id",               # unit identifier
    covariates=["x1", "x2"],    # matching covariates
    lookback=1,                 # periods to look back for baseline
    alpha=0.1,                  # caliper = alpha × pooled_SD
    num_matches=3,              # controls per treated
    replacement="cross_cohort", # control reuse policy (see below)
    model_type="logistic",      # propensity score model
)

# Results
result.balance      # SMD table (polars DataFrame)
result.weights      # unit_id → weight (polars DataFrame)
result.matched_data # treat_id, control_id, difference

Alpha Sweep

Find the optimal caliper automatically:

summary, best = alpha_sweep(
    data, treat="treat", tm="time", entry="entry_time", id="unit_id",
    covariates=["x1", "x2", "x3"],
    alphas=[0.01, 0.02, 0.05, 0.1, 0.15, 0.2],
)
# summary: alpha, match_rate, max|SMD|, all_pass
# best: RollmatchResult from the best alpha

Control Replacement Modes

The replacement parameter controls whether a control unit can be reused across matching:

Mode	replacement=	Within-period reuse	Cross-period reuse	Best for
Unrestricted	"unrestricted" or True	Yes	Yes	Maximizing match rate
Cross-cohort	"cross_cohort" or False	No	Yes	R rollmatch compatibility
Global no	"global_no"	No	No	Unique control assignments

# Each control matched at most once across ALL periods
result = rollmatch(data, ..., replacement="global_no")

# No reuse within a period, but allowed across periods (R rollmatch default)
result = rollmatch(data, ..., replacement="cross_cohort")

# Controls can match multiple treated units freely
result = rollmatch(data, ..., replacement="unrestricted")

Note on "global_no": Because controls are consumed as periods are processed (earliest first), the order of periods affects results. This mode also changes the estimand — later cohorts may get worse matches as the control pool shrinks. Use balance_by_period() to check whether later cohorts suffer.

Backward compatibility: replacement=True maps to "unrestricted" and replacement=False maps to "cross_cohort", matching the original behavior.

Per-Period Balance Diagnostics

Pooled SMD can mask within-cohort imbalance through cancellation. Use balance_by_period() to check each entry cohort individually:

from pyrollmatch import balance_by_period, reduce_data, score_data

reduced = reduce_data(data, "treat", "time", "entry_time", "unit_id")
scored = score_data(reduced, ["x1", "x2", "x3"], "treat")

agg, detail = balance_by_period(
    scored, result.matched_data,
    "treat", "unit_id", "time", ["x1", "x2", "x3"],
)

# agg: covariate × {wtd_mean_smd, median_abs_smd, max_abs_smd, n_periods}
# detail: period × covariate × {n_treated, n_controls, smd}

The aggregate table reports three summary measures per covariate:

• wtd_mean_smd — weighted mean SMD (weighted by n_treated per period)
• median_abs_smd — robust central tendency
• max_abs_smd — most conservative, catches the worst cohort

Post-Matching Diagnostics

from pyrollmatch import balance_test, equivalence_test
from pyrollmatch import reduce_data, score_data

reduced = reduce_data(data, "treat", "time", "entry_time", "unit_id")
scored = score_data(reduced, ["x1", "x2", "x3"], "treat")

# SMD + t-test + variance ratio + KS test
diag = balance_test(scored, result.matched_data,
                    "treat", "unit_id", "time", ["x1", "x2", "x3"])

# TOST equivalence test (Hartman & Hidalgo 2018)
equiv = equivalence_test(scored, result.matched_data,
                         "treat", "unit_id", "time", ["x1", "x2", "x3"])

Propensity Score Models

8 scoring methods, all accessible via the model_type parameter:

Model	model_type	Description	Best for
Logistic	"logistic"	Standard logistic regression (default)	Most cases
Probit	"probit"	Probit model (Φ⁻¹ transform)	Robustness check
GBM	"gbm"	Gradient boosting (HistGradientBoosting)	Non-linear relationships
Random Forest	"rf"	Random forest classifier	High-dimensional, interactions
Lasso	"lasso"	L1-regularized logistic	Variable selection
Ridge	"ridge"	L2-regularized logistic	Multicollinearity
ElasticNet	"elasticnet"	L1+L2 regularized logistic	Combined regularization
Mahalanobis	"mahalanobis"	No propensity model — covariate distance	Direct covariate matching

# Use gradient boosting for propensity scores
result = rollmatch(data, ..., model_type="gbm")

# Use Mahalanobis distance (no propensity model)
result = rollmatch(data, ..., model_type="mahalanobis")

How It Works

Rolling Entry Matching

For staggered adoption, each treated unit enters at a different time. Rolling entry matching:

1. reduce_data() — For each treated unit at entry time t, select covariates at t − lookback. Controls get one observation per treatment entry period.

2. score_data() — Fit propensity model, compute logit-transformed scores.

3. match_all_periods() — For each time period, match treated to closest controls within caliper. Uses block-vectorized numpy broadcasting (not full cross-join).

4. compute_balance() — Compare covariate means/SDs before and after matching.

Key Optimization

R's bottleneck: inner_join(treated, controls, by=time) creates N_treated × N_controls rows.

pyrollmatch instead processes treated in blocks via numpy broadcasting:

Block of 2000 treated × 64K controls = 128M distances = ~1 GB

Never materializes the full cross-product as a DataFrame.

Data Format

Input must be a polars.DataFrame in long panel format (one row per unit per time period):

Column	Type	Description
id	int or str	Unit identifier (e.g., individual, firm, repository)
tm	int	Time period, must be integer and monotonically increasing (e.g., 1, 2, ..., 20)
treat	int (0 or 1)	Time-invariant treatment group indicator. 1 = unit that eventually receives treatment, 0 = unit that never receives treatment. This is NOT a time-varying treatment status — it labels the group, not whether treatment is currently active.
entry	int	Treatment onset period for treated units (the time period when treatment begins). For control units, set this to any value strictly greater than the maximum time period in your data. For example, if your panel spans periods 1–20, use entry=99 or entry=999 for controls. The specific value does not matter as long as it exceeds max(tm).
covariates	float	Matching variables (e.g., pre-computed rolling means of activity measures)

Example:

unit_id | time | treat | entry_time | x1   | x2
--------|------|-------|------------|------|-----
1       | 1    | 1     | 5          | 2.3  | 1.1   <- treated, enters at period 5
1       | 2    | 1     | 5          | 2.5  | 1.0
...
1       | 5    | 1     | 5          | 4.1  | 1.8   <- treatment starts here
...
101     | 1    | 0     | 99         | 1.8  | 0.9   <- control, entry=99 (sentinel)
101     | 2    | 0     | 99         | 1.9  | 1.0

API Reference

Core Functions

Function	Description
rollmatch()	Full rolling entry matching pipeline
alpha_sweep()	Try multiple calipers, select best
reduce_data()	Construct quasi-panel for matching
score_data()	Compute propensity scores (8 models)

Diagnostics

Function	Description
balance_test()	SMD + t-test + variance ratio + KS test
equivalence_test()	TOST equivalence test (Hartman & Hidalgo 2018)
compute_balance()	Covariate balance table (pooled across all periods)
balance_by_period()	Per-period SMD with aggregate summary
smd_table()	Print formatted SMD table

RollmatchResult

Attribute	Type	Description
matched_data	pl.DataFrame	treat_id, control_id, difference
balance	pl.DataFrame	SMD for each covariate
weights	pl.DataFrame	id, weight
n_treated_matched	int	Number matched
n_treated_total	int	Total treated
alpha	float	Caliper used

Validation Against R rollmatch

Tested on identical synthetic data across 15 configurations:

Metric	Result
Propensity score correlation	≥ 0.9999 across all configs
Match count agreement	Within ±3%
Balance quality (max|SMD|)	Both achieve < 0.05

Pair-level overlap varies by caliper tightness (3%–71%) due to different GLM solvers (sklearn vs R glm). This is expected — balance quality is what matters, not pair identity.

Note on matching algorithm: Our greedy matching processes treated units sequentially (per-treated nearest neighbor), while R rollmatch uses a global greedy approach (best-first across all treated). Both are valid greedy algorithms, but may produce different match assignments. Balance quality is comparable.

Performance

Scale	Runtime	Match rate
500 × 2,000	0.03s	100%
1,000 × 3,000	0.05s	100%
5,000 × 15,000	0.10s	100%
10,000 × 30,000	1.7s	99.99%

R rollmatch crashes at 10K treated due to dplyr's 2.1B row limit.

Design Principles

• Polars-first: Native polars DataFrames throughout. No pandas dependency.
• Reproducible: Deterministic with fixed random_state. Same input → same output.
• Scalable: Block-vectorized matching, O(block × N_controls) memory per iteration.
• Validated: 44 tests, R-validated across 15 synthetic configurations.
• Modular: Each step (reduce → score → match → balance) is independently usable.

References

• Witman, A., Acquah, J., Alvelais, A., et al. (2018). "Comparison Group Selection in the Presence of Rolling Entry." Health Services Research, 54(1), 262–270. doi:10.1111/1475-6773.13086
• RTI International. rollmatch R package. https://github.com/RTIInternational/rollmatch
• Hartman, E., & Hidalgo, F. D. (2018). "An Equivalence Approach to Balance and Placebo Tests." American Journal of Political Science, 62(4), 1000–1013. doi:10.1111/ajps.12387

Status & Contributing

This package is under active development. We welcome:

• Bug reports — if you encounter unexpected behavior, please open an issue with a minimal reproducible example
• Feature requests — suggestions for new matching methods, diagnostics, or API improvements
• Contributions — pull requests are welcome; please open an issue first to discuss

Please be cautious when using this package for published research. While we have validated against the R rollmatch package across multiple configurations (see Validation), this is alpha software. We strongly recommend:

1. Cross-checking results against an established implementation (R rollmatch, R MatchIt) for critical analyses
2. Inspecting balance diagnostics carefully before proceeding to estimation
3. Reporting any discrepancies you find via GitHub Issues

License

MIT