61
votes
Accepted
Why is Average Treatment Effect different from Average Treatment effect on the Treated?
The Average Treatment Effect (ATE) and the Average Treatment Effect on Treated (ATT) are commonly defined across the different groups of individuals. In addition, ATE and ATT are often different ...
- 46k
53
votes
Accepted
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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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
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 ...
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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:
...
- 389
17
votes
Accepted
Should I use a machine learning model to calculate propensity score?
There are two approaches for modeling propensity scores. One is to try to approximate the treatment assignment process as closely as possible, and the other is to obtain propensity scores that yield ...
- 36.8k
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 ...
- 102k
15
votes
Accepted
Intuitive explanation for inverse probability of treatment weights (IPTWs) in propensity score weighting?
The propensity score $p(x_i)$ calculated is the probability of subject $i$ to receive a treatment given the information in $X$. The IPTW procedure tries to make counter-factual inference more ...
- 46k
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 ...
- 98.4k
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 ...
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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
Why is Average Treatment Effect different from Average Treatment effect on the Treated?
ATE is the average treatment effect, and ATT is the average treatment effect on the treated.
The ATT is the effect of the treatment actually applied. Medical studies typically use the ATT as the ...
- 277
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 ...
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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 ...
- 38.3k
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
Accepted
Why do stabilized IPW weights give the same estimates and SEs as unstabilized weights?
This is because the marginal structural models you're fitting (objects fitw and fitsw) are so-called saturated models. For ...
- 226
11
votes
Accepted
Propensity score matching vs non-parametric regression
This is a great question and one for which there is no single answer, so I won't attempt to give one to be comprehensive. I'll mention a few topics that might satisfy some of your curiosity and point ...
- 36.8k
11
votes
Is it possible to use propensity score matching (or something similar) to create a control group if you do not yet have outcome data?
Not only can you do that, this this one of the main motivations for using matching methods (such as PSM) over other methods. You can perform the matching to select a subset of individuals that you ...
- 36.8k
10
votes
Difference between covariates and treatment confounders in propensity score matching
The definition of a confounder is somewhat complicated, but VanderWeele & Shpitser (2013) decided
A pre-exposure covariate C is a confounder for the effect of A on Y if
it is a member of some ...
- 36.8k
10
votes
Accepted
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
Accepted
Calculate single absolute standardized difference across levels of a categorical treatment variable cobalt::bal.tab
Author of cobalt here. What the reviewer is requesting doesn't really make a lot of sense. The bias in an effect estimate is a function of the mean difference of ...
- 36.8k
9
votes
Accepted
How can I use Propensity Scores to adjust for survey non-response bias?
The answer to this depends on whether you have a probability sample versus a nonprobability sample, where a probability sample refers to a sample selected using random sampling from the population.
If ...
- 506
8
votes
Accepted
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
How can we justify using "with replacement" in propensity score matching
Matching without replacement can yield very bad matches if the number of comparison observations comparable to the treated observations is small. It keeps variance low at the cost of potential bias.
...
- 38.3k
8
votes
Accepted
Should I put outcome variable in Matchit::matchit ()
DO NOT include the outcome in the propensity score calculation. To analyze your data after matching, don't use match.data(). Just use your original data set, which ...
- 36.8k
8
votes
Accepted
Adjusting the model by propensity scores after propensity score matching
This is definitely possible, and its effectiveness is described in Austin (2017). In general, there is little utility to doing this. If the matching was good, the propensity score distributions should ...
- 36.8k
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 ...
- 36.8k
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, ...
- 3,661
7
votes
Accepted
Alternative analysis to propensity score matching for small sample sizes?
1) If your goal is to make a causal inference, balance is paramount. Although you may have improved balance, if it is not good then your causal inference may still be invalid (/your estimate will ...
- 36.8k
Only top scored, non community-wiki answers of a minimum length are eligible