I admit I'm relatively new to propensity scores and causal analysis.

One thing that's not obvious to me as a newcomer is how the "balancing" using propensity scores is mathematically different from what happens when we add covariates in a regression? What's different about the operation, and why is it (or is it) better than adding subpopulation covariates in a regression?

I've seen some studies that do an empirical comparison of the methods, but I haven't seen a good discussion relating the mathematical properties of the two methods and why PSM lends itself to causal interpretations while including regression covariates does not. There also seems to be a lot of confusion and controversy in this field, which makes things even more difficult to pick up.

Any thoughts on this or any pointers to good resources/papers to better understand the distinction? (I'm slowly making my way through Judea Pearl's causality book, so no need to point me to that)

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    $\begingroup$ Recommend you read Morgan and Winship, 2007. Chapters 4 and 5 do an explicit compare and contrast of regression and matching for causal effect identification. $\endgroup$ Apr 9, 2012 at 19:36
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    $\begingroup$ When you check the balance statistics, you are ensuring that there is not extrapolation between the treatment groups you are comparing with respect to multidimensional covariate space. Regression simply extrapolates without checking for this, so extrapolations can give poor predictions. $\endgroup$ Apr 21, 2016 at 6:26

6 Answers 6


One big difference is that regression "controls for" those characteristics in a linear fashion. Matching by propensity scores eliminates the linearity assumption, but, as some observations may not be matched, you may not be able to say anything about certain groups.

For example, if you are studying a worker training program, you may have all the enrollees be men, but the control, non-participant population be composed of men and women. Using regression, you could regress, income, say, on a participation indicator variable and a male indicator. You would use all your data and could estimate the income of a female had she participated in the program.

If you were doing matching, you could only match men to men. As a result, you wouldn't be using any women in your analysis and your results wouldn't pertain to them.

Regression can extrapolate using the linearity assumption, but matching cannot. All the other assumptions are essentially the same between regression and matching. The benefit of matching over regression is that it is non-parametric (except you do have to assume that you have the right propensity score, if that is how you are doing your matching).

For more discussion, see my page here for a course that was heavily focused on matching methods. See especially Causal Effects Estimation Strategy Assumptions.

Also, be sure to check out the Rosenbaum and Rubin (1983) article that outlines propensity score matching.

Lastly, matching has come a long way since 1983. Check out Jas Sekhon's webpage to learn about his genetic matching algorithm.

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    $\begingroup$ Maybe this is because I am not a statistician, but when it seems you assumed linear regression when the OP asked about regression in general. But I guess the gist is that adding covariates to any kind of regressor makes some assumptions about the input space so it can extrapolate to new examples, and matching is more cautious about what kind of things can be extrapolated. $\endgroup$
    – rrenaud
    Apr 9, 2012 at 19:37
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    $\begingroup$ You do make some assumptions about the functional form of the confounding variables when you estimate the propensity function. You also subsequently match on individuals who have "close" values of the propensity, so I wouldn't immediately assume that propensity matching solves the problem of nonlinear confounder effects. $\endgroup$
    – AdamO
    Aug 7, 2016 at 16:55
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    $\begingroup$ The links are broken. $\endgroup$ Aug 19, 2017 at 6:39

The short answer is that propensity scores are not any better than the equivalent ANCOVA model, particularly with regard to causal interpretation.

Propensity scores are best understood as a data reduction method. They are an effective means to reduce many covariates into a single score that can be used to adjust an effect of interest for a set of variables. In doing so, you save degrees of freedom by adjusting for a single propensity score rather than multiple covariates. This presents a statistical advantage, certainly, but nothing more.

One question which may arise when using regression adjustment with propensity scores is whether there is any gain in using the propensity score rather than performing a regression adjustment with all of the covariates used to estimate the propensity score included in the model. Rosenbaum and Rubin showed that the "point estimate of the treatment effect from an analysis of covariance adjustment for multivariate X is equal to the estimate obtained from a univariate covariance adjustment for the sample linear discriminant based on X, whenever the same sample covariance matrix is used for both the covariance adjustment and the discriminant analysis". Thus, the results from both methods should lead to the same conclusions. However, one advantage to performing the two-step procedure is that one can fit a very complicated propensity score model with interactions and higher order terms first. Since the goal of this propensity score model is to obtain the best estimated probability of treatment assignment, one is not concerned with over-parameterizing this model.



D'Agostino (quoting Rosenbaum and Rubin)

D’agostino, R.B. 1998. Propensity score matching for bias reduction in the comparison of a treatment to a non-randomized control group. Statistical Medicine 17: 2265–2281.

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    $\begingroup$ (+1) There was also an interesting thread about the causality issue in this related question, From a statistical perspective, can one infer causality using propensity scores with an observational study?. $\endgroup$
    – chl
    Mar 22, 2011 at 7:36
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    $\begingroup$ I agree with the general premise of this answer, but when one matches based on the propensity scores it is not the same as plopping all the covariates into the model (and hence is not just a dimension reduction technique). It is not the same if one weights by propensity scores either. $\endgroup$
    – Andy W
    Mar 22, 2011 at 12:46
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    $\begingroup$ I disagree with this answer. Estimated propensity scores are good when they balance covariates in treatment and control groups and bad when don't. Just the same as for a regression conditioning approach. Whether they are 'better' depends only on that property, which will vary from problem to problem. $\endgroup$ Apr 9, 2012 at 19:28
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    $\begingroup$ I'm disagreeing because while the criterion, balance, is the same the two strategies are different, as are their strengths and weaknesses. One may or may not be a better approach, depending on the problem. Indeed, the 'equivalent ANCOVA model' seems to me to be not well-defined. (Equivalent how?) $\endgroup$ Apr 9, 2012 at 21:28
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    $\begingroup$ Right. I now see what 'equivalent' meant, but the sentence that starts 'However' in your quote introduces the relevant difference: in practice prop. scores are estimated separately precisely so they can be way more gnarly than the analysis model. (And there's another difference in the article's following paragraph, not quoted.) $\endgroup$ Apr 9, 2012 at 23:10

A likely obtuse reference, but if you by chance have access to it I would recommend reading this book chapter (Apel and Sweeten, 2010). It is aimed at social scientists and so perhaps not as mathematically rigorous as you seem to want, but it should go into enough depth to be more than a satisfactory answer to your question.

There are a few different ways people treat propensity scores that can result in different conclusions from simply including covariates in a regression model. When one matches scores one does not necessarily have common support for all observations (i.e. one has some observations that appear to never have the chance to be in the treatment group, and some that are always in the treatment group). Also one can weight observations in various ways that can result in different conclusions.

In addition to the answers here I would also suggest you check out the answers to the question chl cited. There is more substance behind propensity scores than simply a statistical trick to achieve covariate balance. It you read and understand the highly cited articles by Rosenbaum and Rubin it will be more clear why the approach is different than simply adding in covariates in a regression model. I think a more satisfactory answer to your question is not necessarily in the math behind propensity scores but in their logic.

  • $\begingroup$ @Andy W See the quote from Rosenbaum and Rubin on the equivalence of regression with covariates and propensity score adjustment in my updated post. $\endgroup$
    – Brett
    Apr 9, 2012 at 21:39

I like to think of PS as a design portion of the study which completely separated from the analysis. That is, you might want to think in terms of design (PS) and analysis (regression etc...). Also, PS porvides a mean of supporting exchangeability for binary treatment; maybe others can comment on whether including the covariates in the outcome model can actaully support exchangebility, or whether one assume exchangeability prior to including the covariates in the outcome model.


Like the person who asked the question, I am relatively new to propensity score analysis. However, my scientific collaborator has deep knowledge and expertise in biostatistics and clinical trial analysis, so I posed this question to him. His answer provides additional insight beyond what was already posted:

When using observational data to estimate the causal effect of a primary exposure (e.g. a drug treatment or intervention) on a health outcome, it is important to balance the treatment and control groups for any other exposures. This balancing attempts to mimic the effect of randomization, which is not possible with observational data. Some exposures are associated with both the treatment and the outcome and thus, meet the definition of confounders. Confounders can be adjusted in multivariable regression models of the health outcome. However, other exposures are associated with the treatment, but NOT the health outcome, and cannot be adjusted in regression models of the outcome. Systematic differences between the treatment and control groups can lead to treatment selection bias.

The proper way to balance the exposures between the treatment and control groups is to use propensity score matching, without consideration of the health outcome variable. Propensity score matching balances the exposures between the treatment and control groups above and beyond what can be accomplished by multivariable regression modeling of the health outcome.

Here is a helpful tutorial, with references, on propensity score analysis from Columbia University's Mailman School of Public Health: https://www.publichealth.columbia.edu/research/population-health-methods/propensity-score-analysis.

Best wishes, Dave


Stat Methods Med Res. 2016 Apr 19.

An evaluation of bias in propensity score-adjusted non-linear regression models.

Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment effect in observational studies. One popular method, covariate adjustment of the propensity score in a regression model, has been empirically shown to be biased in non-linear models. However, no compelling underlying theoretical reason has been presented. We propose a new framework to investigate bias and consistency of propensity score-adjusted treatment effects in non-linear models that uses a simple geometric approach to forge a link between the consistency of the propensity score estimator and the collapsibility of non-linear models. Under this framework, we demonstrate that adjustment of the propensity score in an outcome model results in the decomposition of observed covariates into the propensity score and a remainder term. Omission of this remainder term from a non-collapsible regression model leads to biased estimates of the conditional odds ratio and conditional hazard ratio, but not for the conditional rate ratio. We further show, via simulation studies, that the bias in these propensity score-adjusted estimators increases with larger treatment effect size, larger covariate effects, and increasing dissimilarity between the coefficients of the covariates in the treatment model versus the outcome model.


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