*! version 1.1.1 23jan2017

Title

konfound -- Beta version: For user's model, this command calculates (1) how much bias there must be in an estimate to invalidate/sustain an inference; (2) the impact of an omitted variable necessary to invalidate/sustain an inference for a regression coefficient.

Syntax

konfound
[varlist]
[, options]

Main
varlist a list of variables in the previous model. Users can provide 1 to 10 variable names

Description

konfound (1) Calculates how much bias there must be in an estimate to invalidate/sustain an inference from the immediately preceding model, and interpret in terms of sample replacement.
After running a model (example: linear regression), user can provide the list of variable names, and konfound will produces % bias needed to invalidate/sustain the inference for each variable in the variable list.
The command will also provide sensitivity plots for those variables that are statistically significant in the user's model.

konfound also calculates (2) the impact of an omitted variable necessary to invalidate/sustain an inference for a regression coefficient from a user's model. It
also assesses how strong an omitted variable has to be correlated with the outcome and the predictor of interest to invalidate/sustain the inference.
After running a model (example: linear regression), the user can provide a list of variable names, and konfound will produce the impact of an omitted variable (Frank, 2000) necessary to invalidate/sustain an inference.
The command will also produce the correlation of the omitted variable with the outcome and the predictor of interest necessary to invalidate/sustain the inference.
The command will also provide the observed impact table for all observed covariates in the user's previous model.

Options

sig(#) Significance level of the test; default is 0.05 sig(.05).
To change the significance level to .10 use sig(.1)

nu(#) The null hypothesis against which to test the estimate. The
default is 0 nu(0)

onetail(#) One-tail or two-tail test;
the default is two-tail onetail(0);
to change to one-tail use onetail(1)

uncond(#) calculate the impact and component correlations before or after conditioning on covariates in the model. The default is to
calculate the impact and component correlations after conditioning on covariates uncond(0). To change the calculation to before conditioning (unconditional) on covariates use uncond(1)

rep_0(#) For % bias, this controls the effect in the replacement cases;
the default is null effect (which may or may not be 0) rep_0(0); to force replacing cases with effect of zero use rep_0(1)

non_li(#) Basis for interpreting % bias to invalidate/sustain an inference for non-linear models (e.g., logit or probit). Default is to use the original coefficient non_li(0);
to change the calcuation based on average partial effects use non_li(1).

Remarks

For a graphical illustration of the impact of a confounding variable see https://msu.edu/~kenfrank/research.htm#impact_diagram

For additional details of the calculations in a spreadsheet format and other supporting materials see https://msu.edu/~kenfrank/research.htm#causal.

Examples

. use http://www.ats.ucla.edu/stat/stata/examples/chp/p025b, clear

. rename y2 x5

. regress y1 x1 x4 x5

. konfound x1

. regress y1 x1 x4 x5

. konfound x1, uncond(1)

. regress y1 x1 x4 x5

. konfound x1, sig(0.1) nu(.5) rep_0(1)

Authors

Kenneth A. Frank

Michigan State University

Ran Xu

University of Connecticut

Please email ran.2.xu@uconn.edu if you observe any problems.

References

Frank, K.A. 2000. Impact of a Confounding Variable on the Inference of a Regression Coefficient. Sociological Methods and Research, 29(2), 147-194

Pan, W., and Frank, K.A. 2004. An Approximation to the Distribution of the Product of Two Dependent Correlation Coefficients. Journal of Statistical Computation and Simulation, 74, 419-443

Pan, W., and Frank, K.A., 2004. A probability index of the robustness of a causal inference. Journal of Educational and Behavioral Statistics, 28, 315-337.

*Frank, K. A. and Min, K. 2007. Indices of Robustness for Sample Representation. Sociological Methodology. Vol 37, 349-392. * co first authors.

Frank, K.A., Gary Sykes, Dorothea Anagnostopoulos, Marisa Cannata, Linda Chard, Ann Krause, Raven McCrory. 2008. Extended Influence: National Board Certified Teachers as Help Providers. Education, Evaluation, and Policy Analysis. Vol 30(1): 3-30.

Frank, K.A., Maroulis, S., Duong, M., and Kelcey, B. 2013. What would it take to Change an Inference?: Using Rubin’s Causal Model to Interpret the Robustness of Causal Inferences. Education, Evaluation and Policy Analysis. Vol 35: 437-460.