74
votes
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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 ...
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53
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 ...
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30
votes
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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 ...
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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-...
- 36.8k
24
votes
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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 ...
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18
votes
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matched pairs in Python (Propensity score matching)
The easiest way I've found is to use NearestNeighbors from sklearn:
...
- 389
16
votes
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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 ...
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15
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 ...
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14
votes
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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 ...
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14
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 ...
- 98.4k
13
votes
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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 ...
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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 ...
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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 ...
- 1,063
11
votes
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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 (...
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11
votes
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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 ...
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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 ...
- 36.8k
9
votes
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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 ...
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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 ...
- 98.4k
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 ...
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8
votes
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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 ...
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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, ...
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7
votes
advantages and disadvantages of IPTW vs propensity score matching?
The choice between propensity score matching and weighting seems to be a widely debated topic among statistical sholars. Some thoughts, after having read through many papers of infuriated ...
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7
votes
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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 ...
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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, ...
- 36.8k
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}{\...
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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 ...
- 36.8k
7
votes
Accepted
Why does propensity score matching fail to estimate the true causal effect when OLS works?
As @CloseToC mentioned in the comments, this is because you have a nearly pathological data scenario here. There are a few things that make this scenario "unfair" to matching (i.e., not ...
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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 ...
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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 $(15,3)$ ...
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