مقایسهٔ روش‌ها

	مدل رگرسیون پروبیت	روش متغیرهای ابزاری (IV) برای استنتاج علی	رگرسیون حداقل مربعات معمولی (OLS)
حوزه	اقتصادسنجی	اقتصاد سلامت	اقتصادسنجی
خانواده	Regression model	Process / pipeline	Regression model
سال پیدایش	2018	1990s (modern applications)	2019
پدیدآور	Greene (textbook treatment); classical discrete-choice modelling	Angrist & Pischke (applied econometrics); rooted in econometric theory	Wooldridge (textbook treatment); classical least squares
نوع	Binary discrete-choice model	Method	Linear regression
منبع بنیادین	Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. ISBN: 978-0134461366	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
نام‌های دیگر	probit regression, normit model, Probit Modeli	IV, two-stage least squares, TSLS, causal estimation	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
مرتبط	5	3	5
خلاصه	The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018).	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).