Abstract
Statistical methods that quantify the discourse about causal inferences in terms of possible sources of biases are becoming increasingly important to many social-science fields such as public policy, sociology, and education. These methods are also known as “robustness or sensitivity analyses”. A series of recent works (Frank [2000, Sociological Methods and Research 29: 147–194]; Pan and Frank [2003, Journal of Educational and Behavioral Statistics 28: 315– 337]; Frank and Min [2007, Sociological Methodology 37: 349–392]; and Frank et al. [2013, Educational Evaluation and Policy Analysis 35: 437–460]) on robustness analysis extends earlier methods. We implement these recent developments in Stata. In particular, we provide commands to quantify the percent bias necessary to invalidate an inference from a Rubin causal model framework and the robustness of causal inferences in terms of correlations associated with unobserved variables.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 523-550 |
| Number of pages | 28 |
| Journal | Stata Journal |
| Volume | 19 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 1 2019 |
| Externally published | Yes |
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Keywords
- bias
- causal inferences
- confounding
- konfound
- mkonfound
- pkonfound
- robustness or sensitivity analyses
- st0565
ASJC Scopus subject areas
- Mathematics (miscellaneous)
Cite this
konfound : Command to quantify robustness of causal inferences. / Xu, Ran; Frank, Kenneth A.; Maroulis, Spiro J.; Rosenberg, Joshua M.
In: Stata Journal, Vol. 19, No. 3, 01.09.2019, p. 523-550.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - konfound
T2 - Command to quantify robustness of causal inferences
AU - Xu, Ran
AU - Frank, Kenneth A.
AU - Maroulis, Spiro J.
AU - Rosenberg, Joshua M.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Statistical methods that quantify the discourse about causal inferences in terms of possible sources of biases are becoming increasingly important to many social-science fields such as public policy, sociology, and education. These methods are also known as “robustness or sensitivity analyses”. A series of recent works (Frank [2000, Sociological Methods and Research 29: 147–194]; Pan and Frank [2003, Journal of Educational and Behavioral Statistics 28: 315– 337]; Frank and Min [2007, Sociological Methodology 37: 349–392]; and Frank et al. [2013, Educational Evaluation and Policy Analysis 35: 437–460]) on robustness analysis extends earlier methods. We implement these recent developments in Stata. In particular, we provide commands to quantify the percent bias necessary to invalidate an inference from a Rubin causal model framework and the robustness of causal inferences in terms of correlations associated with unobserved variables.
AB - Statistical methods that quantify the discourse about causal inferences in terms of possible sources of biases are becoming increasingly important to many social-science fields such as public policy, sociology, and education. These methods are also known as “robustness or sensitivity analyses”. A series of recent works (Frank [2000, Sociological Methods and Research 29: 147–194]; Pan and Frank [2003, Journal of Educational and Behavioral Statistics 28: 315– 337]; Frank and Min [2007, Sociological Methodology 37: 349–392]; and Frank et al. [2013, Educational Evaluation and Policy Analysis 35: 437–460]) on robustness analysis extends earlier methods. We implement these recent developments in Stata. In particular, we provide commands to quantify the percent bias necessary to invalidate an inference from a Rubin causal model framework and the robustness of causal inferences in terms of correlations associated with unobserved variables.
KW - bias
KW - causal inferences
KW - confounding
KW - konfound
KW - mkonfound
KW - pkonfound
KW - robustness or sensitivity analyses
KW - st0565
UR - http://www.scopus.com/inward/record.url?scp=85073191693&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073191693&partnerID=8YFLogxK
U2 - 10.1177/1536867X19874223
DO - 10.1177/1536867X19874223
M3 - Article
AN - SCOPUS:85073191693
VL - 19
SP - 523
EP - 550
JO - Stata Journal
JF - Stata Journal
SN - 1536-867X
IS - 3
ER -