Questions tagged [propensity-scores]
The probability of receiving a treatment given a set of observed covariates.
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In a regression to estimate propensity score, how can I build weights proportional to two different quantities?
I have a set of 88 people undergoing a treatment. My focus is on their contacts with a psychiatric service in the year before starting of the treatment, so I want an exact match wrt their previous ...
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9 views
Ok to use PSM to create treatment groups and then plug into CausalImpact? [on hold]
Is it ok to use propensity score matching to create treatment and control groups and then plug these two time series into CausalImpact to estimate your treatment effect? I might want to do this, for ...
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1answer
35 views
Expectation of potential outcomes formula
In Mostly Harmless Econometrics, the author uses the following identity to derive an estimator for the causal effect:
$$E \left[ \frac{Y_i D_i} {p(X_i)} \right] = E \left[Y_{1i} \right]$$
where:
$...
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1answer
24 views
large variance from inverse probability weighting (inverse propensity score)
I heard if the observed data that will be used in the inverse probability weighting method is too small, the estimator based on the weighting will have a large variance.
Could you explain why that is ...
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20 views
Inverse Propensity Score in the paper “Doubly Robust Policy Evaluation and Optimization”
I am currently reading a paper whose link is https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/paper-14.pdf.
In the page 5, or 489, an estimation based on inverse propensity score ...
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0answers
14 views
Transfer Learning in domains other than Image processing and NLP
Can Transfer Learning be applied in domains other than Image processing or NLP? I am trying to apply it on clickstream data (for propensity modeling). Any reference would be greatly appreciated.
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2answers
65 views
Propensity scores in logistic regression models
I have a query after reading a paper, which is about the effectiveness of a medical device. In summary, what the authors did was
1. Generating a propensity score using a multivariable logistic ...
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0answers
17 views
Why do stabilized IPW weights give the same estimates and SEs as unstabilized weights?
In Cole & Hernán (2008), the authors mention that using stabilized weights can decrease the variance of the effect estimate. Regular inverse probability weights use the probability of being in the ...
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1answer
45 views
Using binary outcome variables in real-world data studies must be wrong?
Please be gentle if it's a stupid(ly easy) question:
In medical literature lot's of randomised clinical trials use binary outcome variables, such as 90% reduction in Y, or Y<(a certain threshold). ...
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1answer
37 views
Propensity score matching with MatchIt: Question on missing standard mean differences in balance tab on cobalt package
I am using MatchIt to carry out some propensity score matching, and then using the cobalt package to generate the balance diagnostic. The summary() command on MatchIt has a known bug where it does not ...
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36 views
IPW for the effect of treatment on treated with a continuous treatment
I've been banging my head against the wall trying to figure out how to construct inverse probability of treatment weights (IPTW) for the population average effect of treatment on treated (PATT) with a ...
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1answer
42 views
Why is Propensity Score called a distance measure as well?
I had to use Propensity score matching for my study. I used the MatchIt function in R and I studied what it actually does. I understand that the propensity score is calculated using Logistic ...
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18 views
Creating a Weighted Ratio Based On Size of Customer
I am attempting to created a weighted ratio/score for customers based on the number of support tickets they have entered in a time period against the number of units they have in service with us.
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1answer
22 views
Propensity score: which treatment effect is easier to infer?
I'm currently working on a study where the goal is to estimate the treatment effect of a binary exposure. I want to calculate the Average Treatment Effect (ATE), Average Treatment Effect in the ...
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1answer
89 views
Matching with Multiple Treatments
What's the best way to use matching methods with multiple treatment groups?
I'm assessing the impact of an intervention on an outcome. For my first analysis, I used the MatchIt package (see code below)...
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1answer
55 views
Difference between covariates and treatment confounders in propensity score matching
Here, I have the definition of a propensity score:
Propensity score is defined as the conditional probability of assignment to a treatment given a vector of covariates including the values of all ...
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1answer
126 views
Difference between using a propensity score for matching vs. regression analysis
So I am confused on what the difference is if I match patients based on propensity scores vs. using the propensity score and then applying that into a multivariate regression analysis? Is there a ...
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1answer
34 views
Is repeated propensity score matching over many 0-1-features a valid procedure?
I would like to do a simple linear model where the outcome $y$ is real-valued, but my data matrix $X$ consists of several hundred features that all are $0$-$1$-valued. The number of observations $n$ ...
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1answer
31 views
CBPS Package: PS changes by changing base category [closed]
I am using CBPS package. The treatment variables has three options. Therefore, a multi logit model is run through CBPS. However, the resulting propensity score changes by changing the base category.
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1answer
75 views
Propensity Scores: What is this estimator?
I'm reading The Central Role of the Propensity Score in Observational Studies for Causal Effects in order to understand why Propensity Scores work. I'm kind of new to this and I'm not understanding an ...
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37 views
Posterior distribution of ATE with Bayesian propensity score model?
I am using propensity score weighting (PSW) to estimate the average treatment effect (ATE) of some treatment $D$ on an outcome $Y$ with covariates $X$. I have seen several ways in the literature (both ...
0
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1answer
118 views
Warnings during propensity-score matching: 1: glm.fit: algorithm did not converge 2: glm.fit: fitted probabilities numerically 0 or 1 occurred [duplicate]
I am doing a propensity score matching(nearest neighbor matching) in R with simulated data and I keep getting the above warning messages. please I need help.
The following is my code
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33 views
Why weighted glm coefficients differ from weighted mean?
Let's consider the following dataframe:
...
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1answer
177 views
A difference-in-differences propensity score matching approach
I am facing some challenges using the DID.I have around 500 Items off which 100 are test and its very difficult to find a control group for DID, so I used PSM to find control group using nearest ...
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79 views
Stabilized propensity weights: intuition and ATT formula
The average treatment effect (ATE) of binary treatment T on outcome Y can be estimated using inverse propensity weights:
\begin{equation}\nonumber
\frac{\sum_{i=1}^{N}t_i\hat{\pi}_i^{-1}y_i}{\sum_{i=...
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0answers
56 views
Propensity score matching and DD over different event windows
I'm currently working on a project in which we are trying to investigate the operating (accounting) performance of seasoned equity offerings.
To comprehend the endogeneity problem, we apply ...
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48 views
Impact of propensity model
I have built a propensity model, which gives out probabilities of a customer paying given a collection intervention using a xgboost model. The model has an AOC-ROC of 81% with an accuracy of 77% ...
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18 views
Variables to include in propensity score matching
I want to use propensity score matching to estimate how becoming a mother affect your health.
I define you as a mother, if you in 2006 have children who lives at your home.
Since paneldata i ...
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1answer
129 views
Is it correct to use paired t test after 1:2 matching?
Is it correct to use paired t test after 1:2 propensity score matching? How we can do this in R? I found the following link in SPSS (https://www.researchgate.net/post/how_is_paired_t-...
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28 views
Can selection bias be solved by including control variables?
Omitted variable bias can be solved by including covariates that are omitted. However, can selection bias also be solved by including covariates?
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1answer
74 views
Propensity Score Matching over time?
I have two surveys of households in the same metropolitan areas about the number of transit trips they took. I would like to compare the change in number of transit trips taken by households, between ...
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1answer
70 views
propensity score matching by disease and cox PH regression.. am I right?
Normally I understood propensity score matching is the way to match the treated group and untreated group.
But I noticed some studies that used propensity score matching to match disease and non-...
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1answer
63 views
Propensity score matching: bias adjustment
I'm using propensity score matching to match similar individuals. I.e., I first estimate a propensity score (the probability of treatment conditional on some set of variables) and then match on the ...
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1answer
75 views
Inverse probability weighting (IPW) with positive and negative treatments?
I am studying the effects of the government intervention which can take two directions and is usually represented as follows:
$Treat = \left\{\begin{matrix}
& +1 (intervene), if state A\\
&...
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56 views
Using propensity score matching to find control group for Diff-In-Diff
I am trying out DID as part of coming up with a model to estimate the effect. Below is the scenario: I have around 2000 items for test group(Item count may increase or decrease). Challenge is to find ...
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1answer
82 views
Uplift modelling
I have been trying to build a uplift model which gives incremental probability of a customer responding to a treatment. I am thinking of using pylift library for my model, I had few questions ...
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19 views
R- trouble understanding how setting estimand to “ATE” is affecting matching in “Matching” package
I am working on a project where I am using observational data from patients and trying to find a causal relationship on how Treatment dose affects Patient recovery. Since the data is observational, I ...
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21 views
Incremental Probability apart from the propensity model
I have built a model which predicts if a customer is going to pay given some kind of intervention, say calls. The model is used to generate propensity scores for each customer and then the calling ...
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1answer
84 views
for a multinomial treatment and binary outcome, what is more appropriate, ATC or ATE?
I need help to choose between ATC and ATE for my analysis with multinomial treatment and binary outcome.
In the example below taken from here, it seems that ATT does not sound well for multinomial ...
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1answer
303 views
How to do PSM with panel data using PanelMatch?
I would greatly appreciate if you could let me know how to use PanelMatch for my dataset. Unfortunately, I couldn't find it's manual so I don't know how to find which firms are matched, how to extract ...
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1answer
105 views
how to calculate manually propensity score weights for multinomial treatments where one of them is baseline
I want to get intuition into the calculation of propensity scores (PS) and inverse probability of treatment weights (IPTW) for a multinomial treatment using multinomial regression. One of the ...
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1answer
43 views
Do propensity score matching methods need to factor in the index date in a matched cohort context?
I am working on a comparative effectiveness study where we estimated the propensity of treatment between two groups and are exploring matching on the propensity score. The study period is long, ...
0
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1answer
373 views
1:1 nearest neighbor propensity score matching in R (MatchIt package)
I used 1:1 nearest neighbor propensity score matching in R (MatchIt package) to match samples from an experimental group with a control sample. I used the procedure described here: https://pareonline....
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1answer
53 views
Propensity score matching: covariate balance
I have one concern about propensity score matching's assumption. It seems that what propensity score is doing is to say that the choice of treatment depends on pre-treatment covariates.
Suppose I am ...
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0answers
25 views
Model subgroup- and covariate-specific effects for binary outcome over time
I am currently planning an analysis, in which I try to separate the change in the level of a binary outcome into a subgroup- and a covariate-related effect.
Let's say there are three kind of ...
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0answers
69 views
Calculation of propensity score using cox regression analysis
I want to assess the association of a treatment with survival time in patients.
The background information of patients like age and severity differ between the treatment group and non-treatment group.
...
0
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1answer
105 views
Variance of ATE (Average Treatment Effect) from log-linked gamma model
I have matched my sample using propensity score matching such that each individual has an estimated propensity score of being assigned to a treatment group. Let $T_i$={0,1} be the actual treatment ...
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1answer
129 views
Propensity to pay modelling
I am trying to build a propensity to pay model given an intervention to a customer.
Context:
The population I am dealing with are customers who were supposed to pay some amount on a certain date ...
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1answer
20 views
Propensity Score for observations in RCT study
In theory what would the $e_i$ (propensity scores) be, for $n_i$ observations already randomized into various treatment groups ?
I know $e_i$ (propensity scores) are calculated for $n_i$ ...
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29 views
pospensity score modeling with differences in differences
I want to use a difference in differences specification which groups the data together using propensity scores. To do this is it as simple as obtaining the propensity scores, then taking the ...
