rdrobust

library2.0.0pypypi✓ verified 87d ago

The `rdrobust` Python library (current version 1.3.0) implements local polynomial Regression Discontinuity (RD) point estimators with robust bias-corrected confidence intervals and inference procedures. It is actively maintained and regularly updated, with releases typically aligning with new features or improvements to the underlying R/C++ codebase it wraps.

data

pip install rdrobust

github ↗package ↗

INSTALL

IMPORT

SIG · RDROBUST

rdrobust

datapythonv2.0.0

Install

18.2s avg

Import

5758ms

Disk

460MB

Pass rate

9/ 10

Env Coverage9 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.0.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✓ —

✓ 17.7s

py 3.11

✓ —

✓ 17.13s

py 3.12

✓ —

✓ 17.53s

py 3.13

✓ —

✓ 17.9s

py 3.9

✕ build_error

✓ 20.58s

460MB installed

● package 460MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

rdrobust

✓ import rdrobust as rd

This example simulates data for a regression discontinuity design around a cutoff at `c=0`. It then applies the `rdrobust` function to estimate the treatment effect at the cutoff using the default robust bias-corrected method. It demonstrates preparing data as Pandas Series and accessing the structured output object's summary.

import numpy as np import pandas as pd import rdrobust as rd # Simulate data for a regression discontinuity design np.random.seed(123) n = 500 # Running variable 'x' from -1 to 1 x = np.random.uniform(-1, 1, n) # Outcome 'y' with a jump at x=0 (the cutoff) y = 3 + 2 * x + 4 * (x >= 0) + np.random.normal(0, 1, n) # Convert to pandas Series, which is a common and robust input format y_series = pd.Series(y) x_series = pd.Series(x) # Apply rdrobust with the cutoff c=0 # The output 'r' is an rdrobust.rdrobust_output object r = rd.rdrobust(y_series, x_series, c=0) # Print a summary of the results print("\nRD Robust Results:") print(r.summary()) # You can also access individual components, e.g., the point estimate # print(f"Point Estimate: {r.estimate[0]}")

Debug

Known issues

gotchaInput data for `y` and `x` must be numeric arrays (NumPy arrays or Pandas Series). Passing standard Python lists directly will result in a TypeError.

fix

Convert Python lists to `np.array()` or `pd.Series()` before passing them to `rd.rdrobust()`.

affects: All versions

gotchaThe specified cutoff `c` must fall within the range (min to max) of the running variable `x`. If `c` is outside this range, the function will raise a ValueError.

fix

Ensure that `min(x) <= c <= max(x)`. Adjust the cutoff value or filter your dataset as necessary.

affects: All versions

gotchaWhile `rdrobust` calculates optimal bandwidths by default, understanding the theoretical underpinnings and implications of these choices (`h` for estimation, `b` for bias correction) is crucial. Default bandwidths (e.g., MSE-optimal) may not always be appropriate for all research questions, and sensitivity analyses are recommended.

fix

Consult the `rdrobust` documentation for details on bandwidth selection. Consider trying alternative bandwidths or implementing sensitivity checks to assess the robustness of your results.

affects: All versions

gotcha`rdrobust` is designed for estimation around a single, pre-specified cutoff. If your data has multiple potential cutoffs or a fuzzy RD design, you will need to adapt your analysis strategy (e.g., iterative calls for multiple sharp cutoffs, or using other specialized functions).

fix

For multiple sharp cutoffs, run `rdrobust` separately for each. For fuzzy RD, consider other packages or methodologies tailored for that design.

affects: All versions

Errors

Common errors & fixes

TypeError: 'list' object has no attribute 'shape'

The input variables `y` or `x` were passed as standard Python lists, which lack the 'shape' attribute expected by the underlying numerical operations.

fix

Convert your Python lists to NumPy arrays (`np.array(my_list)`) or Pandas Series (`pd.Series(my_list)`) before passing them to `rd.rdrobust()`.

ValueError: The cutoff c is outside the range of x.

The value specified for the `c` parameter (the cutoff point) does not lie between the minimum and maximum values of your running variable `x`.

fix

Verify that `min(x) <= c <= max(x)`. Adjust `c` to be within the observed range of `x` or ensure your data is appropriately pre-processed.

AttributeError: 'rdrobust_output' object has no attribute 'p_value' (or similar for 'ci', 'se')

Attempting to access an attribute that either doesn't exist by that name, has been renamed, or requires an index because multiple values are stored (e.g., for different null hypotheses or variance types). The `rdrobust_output` object is structured.

fix

Use `r.summary()` to get a comprehensive, formatted overview of all results. To access specific values, inspect the object's attributes (e.g., `dir(r)` or `r.__dict__`) or refer to the `rdrobust` documentation. Often, p-values, estimates, etc., are stored in arrays like `r.p_values[0]` or `r.estimate[0]`.

ModuleNotFoundError: No module named 'rdrobust'

The `rdrobust` Python package has not been installed in your current Python environment, or the environment is not correctly activated.

fix

Open your terminal or command prompt and run `pip install rdrobust`. If using virtual environments, ensure the correct environment is activated before running your Python script.

Upgrade

Version history

2.0.0latest on PyPI · released May 15, 2026

Audit

Dependencies

numpyrequiredRequired for numerical operations and array handling.

scipyrequiredRequired for scientific computing, particularly optimization and statistical functions.

pandasrequiredRecommended for data handling, especially Series and DataFrames as input, though NumPy arrays also work.

Agent activity

17 hits · last 30 days

node

Resources

githubhttps://github.com/rdpackages/rdrobust ↗

packagehttps://pypi.org/project/rdrobust/ ↗