See also my GitHub page.

- Regression Sensitivity Analysis: the Robustness Value and the partial R2

[ description ] [ app ]

The web app "Regression Sensitivity Analysis: the Robustness Value and the Partial R2" makes
the functionalities of the R package sensemakr available on the web.
This web app allows researchers to examine how sensitive their regression estimates are to the inclusion of unobserved confounders, as well as bounding the strength of unobserved confounders based on comparisons to observed covariates. To perform these analyses, the user only needs to provide summary statistics already found in standard regression tables. These results do not rely on any assumptions regarding the functional form of the treatment assignment mechanism nor on the distribution of the unobserved confounders, and hold for multiple confounders, possibly acting non-linearly. For details, please see Cinelli and Hazlett (2020).

- sensemakr: Sensitivity Analysis Tools for OLS (stata)

[ description ] [ software paper ] [ github ] [ ssc ]

The stata module sensemakr implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli and Hazlett (2020).

- sensemakr: Sensitivity Analysis Tools for OLS (Python)

[ github ]

- dml.sensemakr: Sensitivity Analysis Tools for Causal Machine Learning

[ description ] [ theory paper ] [ webpage ] [ github ] - sensemakr: Sensitivity Analysis Tools for OLS

[ description ] [ theory paper ] [ software paper ] [ webpage ] [ github ] [ cran ] - mr-sensemakr: Sensitivity Analysis Tools for Mendelian Randomization

[ description ] [ paper ] [ github ] - generalizing: Generalizing Experimental Results By Leveraging Knowledge of Mechanisms

[ description ] [ paper ] [ github ] - sValues: Measures of the Sturdiness of Regression Coefficients

[ description ] [ github ] [ cran ] - NetworkRiskMeasures: Risk Measures for (Financial) Networks

[ description ] [ github ] [ cran ] - benford.analysis: Benford Analysis for Data Validation and Forensic Analytics

[ description ] [ github ] [ cran ]

The R package dml.sensemakr implements a general suite of sensitivity analysis tools for Causal Machine Learning as discussed in Chernozhukov, V., Cinelli, C., Newey, W., Sharma A., and Syrgkanis, V. (2021). “Long Story Short: Omitted Variable Bias in Causal Machine Learning.”

The R package sensemakr implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli and Hazlett (2020).

The R package mrsensemakr implements sensitivity analysis tools for Mendelian Randomization, as discussed in Cinelli et al (2020). Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy.

The R package generalizing implements methods for generalizing experimental results leveraging the invariance of probabilities of causation, as discussed in Cinelli, C. and Pearl, J. (2020+) “Generalizing Experimental Results By Leveraging Knowledge of Mechanisms.” European Journal of Epidemiology.

The sValues package implements a measure of the sturdiness of regression coefficients (s-value) proposed and discussed by Ed. Leamer.

The Network Risk Measures (NetworkRiskMeasures) package implements a set of tools to analyze systemic risk in (financial) networks in a unified framework.

The Benford Analysis (benford.analysis) package provides tools that make it easier to validate data using Benford’s Law. The main purpose of the package is to identify suspicious data that need further verification.