%0 Journal Article %1 narvaiz2024konfound %A Narvaiz, Sarah %A Lin, Qinyun %A Rosenberg, Joshua M. %A Frank, Kenneth A. %A Maroulis, Spiro J. %A Wang, Wei %A Xu, Ran %D 2024 %I The Open Journal %J Journal of Open Source Software %K causal_inferences programming statistics r-lang %N 95 %P 5779 %R 10.21105/joss.05779 %T konfound: An R Sensitivity Analysis Package to Quantify the Robustness of Causal Inferences %U http://dx.doi.org/10.21105/joss.05779 %V 9 %X Sensitivity analysis, a statistical method crucial for validating inferences across disciplines, quantifies the conditions that could alter conclusions (Razavi et al., 2021). One line of work is rooted in linear models and foregrounds the sensitivity of inferences to the strength of omitted variables (Cinelli & Hazlett, 2019; Frank, 2000).

@article{narvaiz2024konfound, abstract = {Sensitivity analysis, a statistical method crucial for validating inferences across disciplines, quantifies the conditions that could alter conclusions (Razavi et al., 2021). One line of work is rooted in linear models and foregrounds the sensitivity of inferences to the strength of omitted variables (Cinelli & Hazlett, 2019; Frank, 2000). }, added-at = {2024-03-09T12:58:25.000+0100}, author = {Narvaiz, Sarah and Lin, Qinyun and Rosenberg, Joshua M. and Frank, Kenneth A. and Maroulis, Spiro J. and Wang, Wei and Xu, Ran}, biburl = {https://www.bibsonomy.org/bibtex/294a73a08a77fc7ffd4bc87bbc64cffc8/tabularii}, description = {Journal of Open Source Software: konfound: An R Sensitivity Analysis Package to Quantify the Robustness of Causal Inferences}, doi = {10.21105/joss.05779}, interhash = {34f5542dcab0fdf24f39401fe8e346fc}, intrahash = {94a73a08a77fc7ffd4bc87bbc64cffc8}, issn = {2475-9066}, journal = {Journal of Open Source Software}, keywords = {causal_inferences programming statistics r-lang}, month = mar, number = 95, pages = 5779, publisher = {The Open Journal}, timestamp = {2024-03-18T21:56:39.000+0100}, title = {konfound: An R Sensitivity Analysis Package to Quantify the Robustness of Causal Inferences}, url = {http://dx.doi.org/10.21105/joss.05779}, volume = 9, year = 2024 }