Questions tagged [causality]
The relationship between cause and effect.
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Conceptual question: Can you use serial mediation with the same variable at multiple timed to test for a sustained intervention effect over time?
I would like to test whether an intervention boosts self-efficacy at time 1, which then lasts through self-efficacy at time 2, and at time 3, etc, to then have an impact on medication adherence at ...
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How to prove positive effect of a variable with regression?
I am simply trying to prove that giving cash assistance to a company effects their related financial items in a good way.
I calculated the related item's mean for all companies, it's non negative and ...
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Better methodologies to make causal recommendations from correlated data?
I work as a data scientist at a SAAS company. We have an outcome variable, Y, that we consider "success" for our customers. We have a bunch of additional outcome variables X1, X2, X3 that ...
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1answer
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causal inference exercise - “covariate-specific effect”
This graph and questions come from: CAUSAL INFERENCE IN STATISTICS A Primer - Pearl Glymour and Jewell (2016).
We are interested in the effect of $X$ on $Y$. In order to identify it we looking for the ...
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1answer
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Instrumental Variable and “Exclusivity”
In the following DAG:
Can I use IV1 as an instrument for exposure?
In the this video at 4:26 the teacher explains a principle of "exclusivity" for instrumental variables.
Cutoff causes ...
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1answer
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Is it complete mediation when my independent variabel has a significant effect on the dependent variable, only after adding the mediator variable?
I am currently working on my masters thesis and I am testing for mediation. For this mediation I am using the Baron and Kenny method. For path c, you need to see if the effect of the X variable (...
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1answer
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From DAG to SEM, an example
I'm dealing with the following graph, taken from Causality by Pearl (2009):
The book says that in order to identify $\beta$, the coefficient of regression $X = \theta_1 Z + r_1$ is good, and for $\...
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Checking for reverse causality with lead regression, should the lead model be in its original state i.e. no transformation? [duplicate]
Im checking for reverse causality with a regression including leads. The reasoning is that the coefficient of the lead should not be significant if no problem with endogeneity. The original model is:
$...
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How to choose control groups (and how many) to use in CausalImpact?
I'm using the CausalImpact package and noticed that the results are very sensitive to the choice of control groups being fed. My main questions are:
Is there a recommended approach for how to choose ...
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1answer
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Is self-selection for a treatment a problem after all?
I have devised the following R code.
In this simulated dataset, we know that a certain treatment (e.g. a degree, an education) increases income by $ 1,000. Income is also caused by age.
In the first ...
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3answers
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What are the use cases for Propensity Score Matching?
I have asked here whether, in order to establish causal relationships, the treated group and the control group must be similar on all covariates.
The answer was no, if we control for the covariates in ...
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2answers
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Discover causal effects using OLS: does the treated and not treated group need to be similar on all observed variables?
I have one dummy variable, $D$, which equals 1 if the subject received treatment and $0$ otherwise.
My outcome of interest is $Y$. For example, $D$ tells me whether the subject took the drug or a ...
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14 views
Does anyone have experience using causal random forests (e.g. causal_forest from grf R package)? Can they be reliably used in observational studies?
I am considering using causal random forests to detect/quantify potential causal effects of a variable of interest (e.g. radiation dose) on an outcome variable (e.g. some type of radiation-induced ...
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1answer
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What are some examples when the Average Treatment Effect on the Treated/Control (ATT,ATC) is more sought after than the ATE?
I am wondering when and why one may calculate the Average Treatment Effect on the Treated (ATT) or the Average Treatment Effect on the Control (ATC). Is there a specific example or motivation for when ...
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1answer
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Using a Bayesian Additive Regression Trees model for causal inference
Some Context:
I've read this presentation about using a BART model to find out the causal effect of a certain variable with respect to a target variable (say, how much does a specific medicine ...
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1answer
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Why doesn't this work as a backdoor?
In Pearl's book "Causality" on page 124 (http://bayes.cs.ucla.edu/BOOK-2K/ch3-3.pdf) he says:
A set of variables $Z$ satisfies the back-door criterion relative to
an ordered pair $(X_i,X_j)$...
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How to choose covariate adjustment method for effect of treatment estimation? [closed]
When trying to estimate average treatment effect (ATE) how does one choose between the different methods available to adjust for confounders?
For example, how to choose between propensity score block ...
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Propensity Score Matching for Panel Data with Multinomial Outcome on Stata
I have a panel data set from 1999-2019 through which I would like to find whether a divorce would change a respondent's political opinion.
I have identified a number of covariates, most of which are ...
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11 views
Effect of correlation in matching
What is the effect of correlations among observed covariates or between observed and unobserved covariates on the quality of matching, when matching iss done using
Propensity score
(Euclidian/...
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1answer
28 views
DAG: interpretation difference of TOTAL and DIRECT effect in terms of adjusting
Could one explain in simple words how examining the total and direct effect differ in terms of adjusting? How to interpret the findings of these two approaches?
DAG
Minimal sufficient adjustment sets ...
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1answer
37 views
Is the emmeans R package performing causal inference G-computation?
So I am trying to get an understanding of causal inference and how it differs from the usual contrasts. I regularly use the emmeans package in R, and I am wondering when the function emmeans() ...
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5answers
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What does “even if the evidence remains correlational” mean?
From The smart move: we learn more by trusting than by not trusting | Aeon Ideas:
We find the same pattern in other domains. People who trust the media more are more knowledgeable about politics and ...
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1answer
97 views
How to calculate expected Average Treatment Effect on the Treated (ATT) from a data generating process?
I'm running comparisons of different counterfactual modeling methodologies (exact matching, propensity score matching, regression, etc.) on simulated data in order to see which methods produce the ...
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1answer
15 views
derivation of control function approach
In a previous post I have asked about the derivation of $Cov(e, X) = 0$, when using the control function approach.
A follow-up question on this, or, actually, the real question, is: how does the ...
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1answer
24 views
Time length for causal inference experiments
Let's say that I want to run a causal inference experiment, that is an experiment on historical data for an intervention that we were not able to perform a randomized controlled trial for. In the case ...
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Can embeddings function as control variables?
Suppose I am building a linear regression model
$$y = X + Z + \epsilon$$
where $X$ are covariates, $Z$ is a confounder, and $\epsilon$ is noise. $Z$ is a categorical variable with an enormous number ...
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1answer
23 views
Making an instrumental variable by adjusting for few available predictors?
Is my understanding correct. If P1-P3 are unobserved, then I can not use Z as an instrumental variable. However, if they are available and I can adjust for P1-P3, then Z becomes a valid instrumental ...
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21 views
Assignment rules in randomization experiment for causal inference
I'm learning the Casual Inference material on Coursera. I have a question about the following assignment rules in randomization experiment. The first page is for the letter notations. Briefly speaking,...
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1answer
43 views
Does using a probabilistic model for a real-world event make it harder to identify its causes?
I recently read this odd critique of statistics (the author calls it a critique of probability theory, but I think he doesn't understand the difference probability theory and statistics).
http://...
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1answer
71 views
Why conditioning on past treatment in IPTW with time-varying treatments?
Inverse probability weights with time-varying treatments $A_t$ and confounders $L_t$ are defined as the inverse probability of being treated at time $t$ conditional on past treatment and covariate ...
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0answers
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Causality of variables in PCA
Is there any statistical method that shows the causality of a set of variables? In PCA it is possible to determine the correlation between two variables. So, the question is, if X and Y are positively ...
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1answer
37 views
Biased OLS coefficient of endogenous interaction term
Assume we have the following system of equations
Eq. 1: $z = x\gamma + \epsilon$
Eq. 2: $y = z\theta + x\beta + \eta$
where the error terms fulfill standard assumptions. Simulating data from Eq. 1 and ...
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1answer
45 views
Understanding Difference-in-Difference and when to choose alternative model
I am trying to get a good understanding of Difference-in-Difference (DiD) analysis. I will use the sample data constructed in R below.
...
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How to do a meta-analysis to synthesise studies across more than 2 variables?
Are there meta-analysis texts that deal with how to synthesise studies across more than 2 variables, and which also includes synthesising primary studies that deal with more than 2 variables at the ...
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Determining causality between two non-stationary co-integrated time series
I started off using Granger causality test but since the two time series are not stationary and are also co-integrated. Is there another causality test that I can perform which is similar to Granger ...
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2answers
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Mix of terms causation and dependence in 'book of why'?
In the 'book of why' he says:
the listening pattern prescribed by the paths of the causal model
usually results in observable patterns or dependencies in the data
I don't understand, why he says &...
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0answers
19 views
Term for variable which is both confounder and mediator
Is there a term for a variable that is both a mediator and a confounder at the same time? By this a mean a variable that both influences the exposure and the outcome but there is also an influence ...
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1answer
28 views
Connecting potential outcomes, SUTVA, and regression methods
I'm trying to articulate the differences between identification in regression models and assumptions with the potential outcomes framework. In particular what (if anything) does SUTVA add beyond an ...
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1answer
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Addressing the Ashenfelter’s dip
I am running a difference in difference (DiD) model on the effects of job-training on earnings. My data consists of a staggered adoption design whereby units receive treatment at different times, but ...
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2answers
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Forecasting $X_{t+2}$ for causal AR(p)
Let $X_t$ be a causal $AR(p)$ process. Compute a linear forecast $X_{t+2}$ based on $X_1, X_2, ..., X_t$ for $t \geq p+1$.
If $AR(p)$ is causal it means that it can be rewritten as a linear process: $...
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1answer
81 views
Do-Calculus for Causal Diagram 7.5 from “The Book of Why” (napkin problem)
In "The Book of Why" the below causal diagram is described as the "simplest model" where estimation of the causal effect goes beyond front and back-door adjustment and thus ...
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Modeling ideas on biologics manufacturing data with complex genealogy
This question is about finding options to model the data for a biologics drug manufacturing. The manufacturing process is divided into upstream and downstream, where the output material from an ...
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1answer
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Is a three-way fixed effects model equivalent to a triple difference estimator?
I have a conceptual question about the fixed effects model and the difference-in-differences (DD) estimator. Since a two-way fixed effects model is equivalent to a DD estimator, I was wondering if ...
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0answers
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2SLS approach using lagged dependent variable as an instrument
I was wondering if the lagged dependent variable can be a valid instrument or not.
In my data, policy intervention (x) is made depending on the outcome (y) in the past. (not depending on the effect (b)...
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1answer
60 views
How does control function approach resolve endogeneity?
Suppose I want to estimate
$$Y = \beta_1 + \beta_2 X + \varepsilon$$
Now I know that $X$ and $Y$ are also reversely related
$$X = \gamma_1 + \gamma_2 Y + \xi$$
such that $Cov(\varepsilon,X) \neq 0$.
$...
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1answer
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How to use causal inference to understand the impact of COVID-19 on different groups?
I'm experienced in data science but new to causal methods. I'm trying to figure out how to frame a question I want to investigate in a causal way.
I'm interested in seeing if COVID-19 has impacted two ...
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4answers
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The fallacy of correlating some time series values with specific time points: is there a specific name for it or are there references?
Intro / Background / Example
A recent article connecting pollen with covid-19 has gone viral this week.
Higher airborne pollen concentrations correlated with increased SARS-CoV-2 infection rates, as ...
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1answer
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Caliper output Matchit [closed]
I was examining output of the MatchIt package in R using this code
...
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0answers
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Cross-sectional study design with no control group compares 2 treatment arms
There is a trend among medical research that assesses cross sectional (singular point in time) treatment comparisons between 2 treated groups in retrospective data. In other words the a person who ...
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2answers
109 views
Expectation of structural equation
I am trying to learn about structural equations, and in this post here Correlation, regression and causal modeling I am having difficulties trying to prove the answer.
The problem is, given structural ...
