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72 votes
Accepted

Why do we do matching for causal inference vs regressing on confounders?

As I see it, there are two related reasons to consider matching instead of regression. The first is assumptions about functional form, and the second is about proving to your audience that functional ...
Noah's user avatar
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51 votes
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Propensity score matching - What is the problem?

It's true that there are not only other ways of performing matching but also ways of adjusting for confounding using just the treatment and potential confounders (e.g., weighting, with or without ...
Noah's user avatar
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30 votes
Accepted

How can this counterintiutive result with the Mahalanobis distance be explained?

"Why not draw a picture?" asks @mhdadk. Why not indeed? Here are contours of the Mahalanobis distance/Gaussian likelihood centred at T (17, 4) (open ...
Thomas Lumley's user avatar
29 votes

Is Propensity Score Matching a "MUST" for Scientific Studies?

Propensity score methods are one type of method used to adjust for confounding. There are several other methods that rely on different assumptions. Some of the most popular include difference-in-...
Noah's user avatar
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22 votes
Accepted

What are the pros and cons of using mahalanobis distance instead of propensity scores in matching

Mahalanobis distance matching (MDM) and propensity score matching (PSM) are methods of doing the same thing, which is to find a subset of control units similar to treated units to arrive at a balanced ...
Noah's user avatar
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18 votes
Accepted

matched pairs in Python (Propensity score matching)

The easiest way I've found is to use NearestNeighbors from sklearn: ...
volodymyr's user avatar
  • 389
16 votes
Accepted

What are the use cases for Propensity Score Matching?

You need to distinguish between uses of propensity scores for matching of cases versus for more general adjustments. The discussion on this page suggests that there isn't much of a use case for ...
EdM's user avatar
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14 votes

Propensity score matching - What is the problem?

@Noah's answer is superb and qualifies as a mini review article. To me, the severe problems with PS matching are topped off by (1) it does not represent reproducible research in that the choice of ...
Frank Harrell's user avatar
14 votes
Accepted

Do I need to adjust OLS standard errors after matching?

Following up on Dimitriy's comment, which I agree with. There are (at least) three sources of uncertainty when performing a propensity score matching analysis: 1) the estimation of the PS, 2) the ...
Noah's user avatar
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13 votes
Accepted

Why is Propensity Score Matching better than just Matching?

The procedure you described is not propensity score matching but rather propensity score subclassification. In propensity score matching, pairs of units are selected based on the difference between ...
Noah's user avatar
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13 votes

What are the use cases for Propensity Score Matching?

Propensity score (PS) analysis has many problems in general, and matching is especially problematic. I prefer covariate adjustment for a spline function of the logit of PS if you need propensity ...
Frank Harrell's user avatar
12 votes

Why is Propensity Score Matching better than just Matching?

Let's step back and think more broadly about how you could match given some data X. Exact or Cell Matching This is hard to do with continuous Xs. You could try rounding/discretizing each variable, but ...
dimitriy's user avatar
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12 votes

Is Propensity Score Matching a "MUST" for Scientific Studies?

As Alexis pointed out, propensity score matching (PSM) is one of many tools we have in causal inference. Another one is Inverse Probability Weighted Estimator (IPWE). You can also use causal discovery ...
mribeirodantas's user avatar
11 votes
Accepted

How exactly to evaluate Treatment effect after Matching?

The documentation for Matching is sadly fairly incomplete, leaving what it does quite mysterious. What is clear is that it takes a different approach from Stuart (...
Noah's user avatar
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10 votes
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Matching with Multiple Treatments

I recommend taking a look at Lopez & Gutman (2017), who clearly describe the issues at hand and the methods used to solve them. Based on your description, it sounds like you want the average ...
Noah's user avatar
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10 votes
Accepted

Is Coarsened Exact Matching superior to other matching methods in case-control studies?

CEM does not allow you to estimate the ATE. This is because the matched units in each treatment group will not resemble the overall sample. If no treated units are unmatched, you can estimate the ...
Noah's user avatar
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9 votes
Accepted

Possibly unmeasured confounder and other highly correlated controls

Although it is true that confounding is due to common causes of the treatment and outcome, a confounder does not have to cause both the treatment and the outcome. It needs to lie along an open ...
Noah's user avatar
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8 votes
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Analysis strategy for rare outcome with matching

Removing good data from an analysis is scientifically suspect in my humble opinion, and naive matching methods are inefficient. It may be very easy to adjust for patient characteristics using ...
Frank Harrell's user avatar
8 votes
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McNemar or Fisher exact test for propensity score matched data?

This is definitely an ongoing debate in the literature, but at this point the evidence points to using paired analysis to compute standard errors and p-values. Although the goal of matching is to ...
Noah's user avatar
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8 votes
Accepted

Are only confounders used to generate propensity scores for propensity score matching/IPW?

The balancing property of propensity scores has nothing to do with whether the predictors are confounders or not. It is a purely statistical property that has nothing to do with causal inference or ...
Noah's user avatar
  • 35k
8 votes
Accepted

Resample from a sample to match a desired distribution

Assign observations a weight according to the likelihood ratio of $q(x)$ to $p(x)$ and sample according to those weights. For instance, suppose I obtain my first sample $X$ as standard normal but I ...
AdamO's user avatar
  • 63.8k
7 votes

Analysis strategy for rare outcome with matching

Based on the comments and the availability of such a large control group, I would probably advise to do in a step first exact matching on age groups and sex, and perhaps common disease groups. Hereby, ...
Arne Jonas Warnke's user avatar
7 votes
Accepted

Is it possible of overfit using Propensity score matching with the MatchIt R package?

First, I would caution anyone without a background in applied statistics from performing advanced analyses like propensity score matching. The ease of the software makes it seem like the procedure ...
Noah's user avatar
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7 votes
Accepted

Can matched samples be analysed using regression?

See my answer here for a discussion of two approaches to standard error estimation for matched samples. For a discussion on standard error and effect estimation when using matching with replacement, ...
Noah's user avatar
  • 35k
7 votes

MatchIt output using Coarsened Exact Matching

The effective sample size (ESS) is the size of an unweighted sample carrying approximately the same precision as the weighted sample in question. The formula for the ESS is $$ESS = \frac{(\sum w)^2}{\...
Noah's user avatar
  • 35k
7 votes

Matching with a continuous treatment

Matching is not well developed for continuous treatments, but weighting is. The CBPS package implements weighting for categorical or continuous treatments using the ...
Noah's user avatar
  • 35k
7 votes
Accepted

Justifying a smaller control group in survey study

I think you've answered your own question. If your goal is to independently assess the psychometric properties within the control group, collect enough data to do that. If your goal is only to compare ...
Eoin's user avatar
  • 9,475
7 votes

How can this counterintiutive result with the Mahalanobis distance be explained?

Very qualitative but visual explanation: since your covariance matrix is not diagonal, the distribution of the samples is going to be tilted with respect to the main axes. Both $(17,4)$ and $(15,3)$ ...
Camille Gontier's user avatar
6 votes
Accepted

Can we perform matching on post-treatment variables?

I would suggest to match only on pre-treatment variables and not on post-treatment variables. If we match on post-treatment variables it is extremely plausible we induce selection bias in our sample ...
usεr11852's user avatar

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