# Questions tagged [causality]

The relationship between cause and effect.

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### Causal assumptions when treating time series as a bunch of points

Suppose there are two time series, $x_t$ and $y_t$, that capture daily counts of some sort. $x_t$ is believed to have causal impact on $y_t$. Suppose further that a simple regression is fit to the ...
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### Parameter identification and causal identification

When people say identification, do "parametric identification" and "causal identification" mean completely different? Ex) When performing ML estimation, the sentence that one ...
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### Heterogeneous Treatment Effects with Continuous Treatment (e.g. using BART)

Overview: Most of the causal inference literature (both theoretical and applied), I have seen on heterogeneous treatment effects, only considers the case with a binary treatment $T\in\{0,1\}$. However,...
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### What's the DGP in causal inference?

This question come from this discussion (Under which assumptions a regression can be interpreted causally? ). That discussion touch too arguments and is not possible to speak about all things there. ...
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### How to create a pseudo panel from a repeated cross-sectional dataset?

I am currently writing my Master thesis that focuses on the determinants of teenage pregnancy using a repeated cross-section from the Performance Monitoring for Action (PMA) project. It includes ...
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### The Regression Rule for Identification: problem 3.8.1(c) in Causal Inference in Statistics: A Primer

Consider the following causal model: For each of the parameters in the model, write a regression equation in which one of the coefficients is equal to that parameter. Identify the parameters for ...
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### Relevance of overfitting while examining causal effects

Let's assume that we have a well defined directed acrylic graph (DAG) showing correct causal relationships between variables. And this DAG tells us that we need to adjust for 7 variables to analyse ...
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### DAG: causal direction unknown

I have a DAG as follows. Interested in the direct effect of E on O. Plus interested in publishing an article with a sound methodology. There definitely (read: highly probably) is a causal direction ...
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### Consistent estimator - consistent with what exactly?

Lets assume, that the real DGP (real world data) is generated from the model: $$y_i = \beta_0 + \beta_1x_{1i} + \beta_2x_{2i} + \varepsilon_i$$ Lets further assume, that $x_1$ and $x_2$ are correlated....
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### Testing for causality with Support Vector Machines

Can a support vector machine (SVM) be used to test for causality between 2 or more variables? I know that the original purpose for SVM is classification. I also know that there is a variation of the ...
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### How to determine an appropriate “closeness” threshold when matching for causal inference?

Say I have a [yes/no] treatment variable (e.g. the customer complained about their order) and I want to estimate the causal impact of this "treatment" on the average customer's future spend. ...
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### Effect of duration of treatment on time to event outcome (overall survival)

I am trying to understand how to design an analysis for the following situation. We have the following data collected retrospectively on a group of patients: Age, Sex, Race, and other variables ...
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### Implementations for Conditional Average Treatment Effects that can be trained incrementally

I am currently working on a very large dataset (billions of rows) of A/B test data and want to implement some methods to estimate conditional average treatment effects. I basically need a forecast ...
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### DAG: when should we use variables marked as “adjusted”?

daggity.net allows to define variables as "adjusted". Manual gives the following definition for adjusted variables: for variables that have been adjusted for in a statistical analysis In the ...
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### Assessing for causality after genetic matching - how to use weights

I am conducting an analysis of the effect of COPD on particular outcomes after surgery. I have found that utilizing the matchit package with the ...
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### DAGs: instrumental and adjusted variables

While drawing DAGs, we can define variables as exposure, outcome and unobserved etc. Could you please explain, what are instrumental and adjusted variables?
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### Regression after matching

I performed 1:1 nearest neighbor matching on 16 covariates using matchit package on R. Covariate balance looks good for most covariates, but there are some that looks less than ideal. I then ran ...
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### Judea Pearl do-calculus using DoWhy package

I have a dataset in which the columns are the variables X,Y,Z,W,A,B. I would like to evaluate $P(Y|do(X=x))$. In the package DoWhy for Python, there is the example: ...
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### Transforming Heterogeneous Treatment Effect Models (in EconML) into Average Treatment Effect Model (from DoWhy)

This question relates to the steps one would need to take in order to reproduce an answer from the DoWhy tutorial, using the EconML library code for heterogeneous causal effects. In DoWhy, there is ...
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### Is Bayesian estimation useful for causal analyses?

Is Bayesian estimation useful for causal analyses? For analyses like randomized experiments or even observational studies of natural experiments, we want unbiased estimators of the causal effect (...
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### Regression where predictors are correlated with past values of y

Setup We are interested in estimating a model for the following setup: $Y_t=\beta_0 + \beta_1^{'}X^{'}_t + \epsilon_t$ $COV(X^{'}_t,Y_{t-1,t-2,...,1} | X_{t-1,t-2,...,1}) = 0$ Where $\epsilon_t$ is ...
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### Recommended books on Mediation Analysis?

I am interested in self learning Mediation Analysis. I have an MSc in Statistics, and I was wondering what would be an appropriate textbook to dig into this area. I would like something that combines ...
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### Modeling and counteracting exposure bias in recommender systems

I am looking for best strategies to train a new recommendation model from the biased data (due to modeling bias from the previous model). For e.g. Lets assume I have an e-commerce site and initially I ...
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### Notation for independence of potential outcomes

If we say (binary) treatment status, t is independent of potential outcomes, $\{y_1,y_o\}$, it is usually writing as $t_i \perp \!\!\! \perp \{y_{i1},y_{io}\}$ . I take this to mean intuitively, for ...
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### Failing to fully control for a variable

Lets assume we want to perform a 'reduced-form' causal analysis to evaluate the impact of a program on the dependent variable of interest. (However the question is more universal). Lets further assume,...
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### Can I infer causality group of correlated random variables?

I have a group of random variables (vectors of numbers) that are highly correlated $(corr>0.9)$. I wonder if I can infer which of these variables is the dominant one: the one that causes the other ...
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### Causal inference for multiple treatments with an observed set of properties

Note: I have rewritten this question quite a lot, because pzivich's answer made me realize that I had not formulated it accurately enough . In order to give the original context of pzivich's answer, I ...
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### How can I proceed when causal directions are not that clear? An example is provided

I working with observational data and defining assumptions for DAG seems to be more complex than often in examples provided in textbooks. For me, it would be much easier to just skip DAG part and ...